From ae0bf2344d65c0768fbd619e9a061ab9afba5a82 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 19 Sep 2020 12:24:47 +0100
Subject: [PATCH 01/35] Return metabolite ID with msn annotation results

---
 metaboblend/build_structures.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 013143c..871aa6d 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -426,7 +426,7 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                     [k + "," + str(i) + "\n" for k, i in zip(structure_frequency.keys(), structure_frequency.values())])
 
         if yield_smi_dict:
-            yield structure_frequency
+            yield {ms_id: structure_frequency}
 
     db.close()
 

From de6eab2d2014675405475e9dbd250d13c9ce55c4 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 19 Sep 2020 12:29:42 +0100
Subject: [PATCH 02/35] Rewrite MSn method for limiting connectivity to
 fragment edges only

---
 metaboblend/build_structures.py | 67 +++++++++++++++++++++++++--------
 metaboblend/databases.py        | 32 +++-------------
 2 files changed, 58 insertions(+), 41 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 871aa6d..b32b3f5 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -185,8 +185,10 @@ def reindex_atoms(records):
 
             if atom.GetIdx() in record["degree_atoms"]:
                 atoms_available.append(new_idx)
+
             if atom.GetIdx() in record["dummies"]:
                 atoms_to_remove.append(new_idx)
+
             if atom.GetIdx() in record["bond_types"]:
                 bond_types[new_idx] = record["bond_types"][atom.GetIdx()]
                 all_bond_types[i] += record["bond_types"][atom.GetIdx()]
@@ -606,17 +608,26 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
     db = SubstructureDb(path_substructure_db, path_connectivity_db)
     tolerance = 0.001
 
-    if prescribed_mass is None:  # standard build method
+    if prescribed_mass is None:
         exact_mass__1 = round(exact_mass)
-        exact_mass__0_0001 = round(exact_mass, 4)
 
     else:  # prescribed substructure build method
+        if ((prescribed_mass / 1000000) * ppm) > 0.001:
+            fragment_tolerance = round((prescribed_mass / 1000000) * ppm, 4)
+        else:
+            fragment_tolerance = 0.001
+
+        prescribed_subset = db.select_mass_values("0_0001", [round(prescribed_mass)], table_name)
+        prescribed_subset = [m for m in prescribed_subset[0] if abs(m - prescribed_mass) <= fragment_tolerance]
+
+        if len(prescribed_subset) == 0:
+            return set()
+
         loss = exact_mass - prescribed_mass
         exact_mass__1 = round(loss)
-        exact_mass__0_0001 = round(loss, 4)
 
-        if ((loss / 1000000) * ppm) > 0.001:
-            tolerance = round((loss / 1000000) * ppm, 4)
+        if ((exact_mass / 1000000) * ppm) > 0.001:
+            tolerance = round((exact_mass / 1000000) * ppm, 4)
 
         max_n_substructures -= 1  # we find sets of mols that add up to the loss, not the precursor mass
 
@@ -630,7 +641,7 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
 
     integer_subsets = list(subset_sum(integer_mass_values, exact_mass__1, max_n_substructures))
 
-    configs_iso = db.k_configs(prescribed_mass is not None)
+    configs_iso = db.k_configs()
     if path_smi_out is not None:
         smi_out = open(path_smi_out, out_mode)
 
@@ -641,25 +652,28 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
 
         # refine groups of masses to 4dp mass resolution
         exact_mass_values = db.select_mass_values("0_0001", integer_subset, table_name)
-        exact_subsets = []
+
+        if prescribed_mass is not None:
+            exact_mass_values = [prescribed_subset] + exact_mass_values
 
         # use combinations to get second group of masses instead of subset sum - subset sum is integer mass only
+        exact_subsets = []
         for mass_combo in itertools.product(*exact_mass_values):
-            if abs(sum(mass_combo) - exact_mass__0_0001) <= tolerance:
+            if abs(sum(mass_combo) - exact_mass) <= tolerance:
                 exact_subsets.append(mass_combo)
 
         if len(exact_subsets) == 0:
             continue
 
-        if prescribed_mass is not None:  # add fragments mass to to loss group
-            exact_subsets = [subset + (round(exact_mass - loss, 4),) for subset in exact_subsets]
-
         # refines groups based on ecs and gets substructures from db (appends to substructure_subsets)
         for exact_subset in exact_subsets:
             substructure_subsets += build_from_subsets(exact_subset, mf=mf, table_name=table_name, db=db)
 
     with multiprocessing.Pool(processes=ncpus) as pool:  # send sets of substructures for building
-        smi_lists = pool.map(partial(substructure_combination_build, configs_iso=configs_iso), substructure_subsets)
+        smi_lists = pool.map(
+            partial(substructure_combination_build, configs_iso=configs_iso, prescribed_structure=prescribed_mass is not None),
+            substructure_subsets
+        )
 
     smis = set([val for sublist in smi_lists for val in sublist])
 
@@ -795,7 +809,7 @@ def build_from_subsets(exact_subset, mf, table_name, db):
     return substructure_subsets
 
 
-def substructure_combination_build(substructure_subset, configs_iso):
+def substructure_combination_build(substructure_subset, configs_iso, prescribed_structure):
     """
     Final stage for building molecules; takes a combination of substructures (substructure_combination) and builds them
     according to graphs in the substructure database. May be run in parallel.
@@ -811,12 +825,25 @@ def substructure_combination_build(substructure_subset, configs_iso):
     smis = []
 
     for substructure_combination in itertools.product(*substructure_subset):
+        substructure_combination[0]["fragment"] = True
         substructure_combination = sorted(substructure_combination, key=itemgetter('atoms_available', 'valence'))
 
         v_a = ()
-        for d in substructure_combination:
+        if prescribed_structure is not None:
+            fragment_indexes = []
+        j = -1
+        for i, d in enumerate(substructure_combination):
             v_a += (tuple(d["degree_atoms"].values()),)  # obtain valence configuration of the set of substructures
 
+            for atom_available in tuple(d["degree_atoms"].values()):
+                j += 1
+
+                try:
+                    if prescribed_structure is not None and d["fragment"]:
+                        fragment_indexes.append(j)
+                except KeyError:
+                    continue
+
         if str(v_a) not in configs_iso:  # check mols "fit" together according to the connectivity database
             continue
 
@@ -826,6 +853,16 @@ def substructure_combination_build(substructure_subset, configs_iso):
             continue  # check that bond types are compatible (imperfect check)
 
         for edges in configs_iso[str(v_a)]:  # build mols for each graph in connectivity db
+            if prescribed_structure is not None:
+                non_fragment_edges = False
+
+                for edge in edges:  # check that edges only connect to fragment ion
+                    if edge[0] not in fragment_indexes and edge[1] not in fragment_indexes:
+                        non_fragment_edges = True
+
+                if non_fragment_edges:
+                    continue
+
             mol_e = add_bonds(mol_comb, edges, atoms_available, bond_types)  # add bonds between substructures
 
             if mol_e is None:
@@ -842,7 +879,7 @@ def substructure_combination_build(substructure_subset, configs_iso):
                 continue
 
             try:  # append the canonical smiles of the final structure
-                smis.append(Chem.MolToSmiles(mol_out))
+                smis.append(Chem.MolToSmiles(mol_out, isomericSmiles=False))
             except RuntimeError:
                 continue  # bad bond type violation
 
diff --git a/metaboblend/databases.py b/metaboblend/databases.py
index 0f2d449..b2aa168 100644
--- a/metaboblend/databases.py
+++ b/metaboblend/databases.py
@@ -395,14 +395,12 @@ def select_mfs(self, exact_mass, table_name, accuracy):
 
         return self.cursor.fetchall()
 
-    def k_configs(self, fragment_edges_only=False):
+    def k_configs(self):
         """
         Obtains strings detailing the valences for each substructure in a connectivity graph and the ID of the related
         PKL file. Used to match a set of substructures to the correct set of non-isomorphic graphs in the connectivity
         database.
 
-        :param fragment_edges_only: If true, only include sets of edges that connect to the fragment ion.
-
         :return: Dictionary containing the valences as keys and PKL IDs as values.
         """
 
@@ -414,28 +412,10 @@ def k_configs(self, fragment_edges_only=False):
 
         for record in records:
             configs[str(record[1])] = []
-            fragment_atoms = [i for i in range(len(eval(record[1])[0]))]
 
             for path in self.paths(pickle.loads(record[0])):
-                if fragment_edges_only:
-                    all_bonds_connect_to_fragment = True
-
-                    for edge in path:
-                        if edge[0] not in fragment_atoms and edge[1] not in fragment_atoms:
-                            all_bonds_connect_to_fragment = False
-
-                    # check that all bonds connect to fragment ion
-                    if all_bonds_connect_to_fragment:
-                        configs[str(record[1])].append(path)
-
-                else:
-                    # for normal building, there is no fragment ion
                     configs[str(record[1])].append(path)
 
-            # delete keys for which there are no valid edge sets
-            if len(configs[str(record[1])]) == 0:
-                del configs[str(record[1])]
-
         return configs
 
     def paths(self, tree, cur=()):
@@ -708,7 +688,7 @@ def get_substructure(mol, idxs_edges_subgraph):
     except:
         return
 
-    return {"smiles": Chem.MolToSmiles(mol_out),  # REORDERED ATOM INDEXES
+    return {"smiles": Chem.MolToSmiles(mol_out, isomericSmiles=False),  # REORDERED ATOM INDEXES
             "mol": mol_out,
             "bond_types": bond_types,
             "degree_atoms": degree_atoms,
@@ -811,8 +791,8 @@ def filter_records(records):
             if len(atom_check) > 0:
                 continue
 
-            smiles_rdkit = Chem.MolToSmiles(mol)
-            smiles_rdkit_kek = Chem.MolToSmiles(mol, kekuleSmiles=True)
+            smiles_rdkit = Chem.MolToSmiles(mol, isomericSmiles=False)
+            smiles_rdkit_kek = Chem.MolToSmiles(mol, isomericSmiles=False, kekuleSmiles=True)
 
             if "+" in smiles_rdkit_kek or "-" in smiles_rdkit_kek or "+" in smiles_rdkit or "-" in smiles_rdkit:
                 continue  # only neutral molecules are compatible
@@ -1029,7 +1009,7 @@ def create_substructure_database(hmdb_paths: Union[str, bytes, os.PathLike],
     db.close()
 
 
-def update_substructure_database(hmdb_path: Union[str, bytes, os.PathLike],
+def update_substructure_database(hmdb_path: Union[str, bytes, os.PathLike, None],
                                  path_substructure_db: Union[str, bytes, os.PathLike],
                                  ha_min: Union[int, None] = None,
                                  ha_max: Union[int, None] = None,
@@ -1191,7 +1171,7 @@ def insert_substructure(lib, cursor, record_dict, substructures_only, max_atoms_
         if lib["valence"] > max_degree:
             return
 
-    smiles_rdkit = Chem.MolToSmiles(lib["mol"])  # canonical rdkit smiles
+    smiles_rdkit = Chem.MolToSmiles(lib["mol"], isomericSmiles=False)  # canonical rdkit smiles
 
     exact_mass = calculate_exact_mass(lib["mol"])
     els = get_elements(lib["mol"])

From cf2378ea38839f08a2362ee740b696f675e9d48f Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 19 Sep 2020 13:00:17 +0100
Subject: [PATCH 03/35] Add option for use of smiles without non-structural
 isomeric information

---
 metaboblend/build_structures.py | 27 +++++++++++++------
 metaboblend/databases.py        | 48 +++++++++++++++++++++------------
 tests/test_databases.py         | 14 +++++-----
 3 files changed, 58 insertions(+), 31 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index b32b3f5..f3707a3 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -291,7 +291,8 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                  write_fragment_smis: bool = False,
                  minimum_frequency: Union[int, None] = None,
                  hydrogenation_allowance: int = 2,
-                 yield_smi_dict: bool = True) -> Union[Sequence[Dict[str, int]], None]:
+                 yield_smi_dict: bool = True,
+                 isomeric_smiles: bool = False) -> Union[Sequence[Dict[str, int]], None]:
     """
     Generate molecules of a given mass using chemical substructures, connectivity graphs and spectral trees or
     fragmentation spectra. Final structures and rankings are yielded by the function as a dictionary and/or written in
@@ -357,6 +358,8 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
     :param yield_smi_dict: If True, for each input molecule the function yields a dictionary whose keys are SMILEs
         strings and values are the number of `fragment_masses` by which the structure was generated. Else, returns None.
 
+    :param isomeric_smiles:  If True, writes smiles with non-structural isomeric information.
+
     :return: For each input molecule yields a dictionary whose keys are SMILEs strings for the generated
         structures and values are the number of `fragment_masses` by which the structure was built (unless
         `yield_smi_dict = False`).
@@ -415,7 +418,8 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                     out_mode="a",
                     table_name=table_name,
                     ncpus=ncpus,
-                    clean=False
+                    clean=False,
+                    isomeric_smiles=isomeric_smiles
                 ))
 
             for smi in fragment_smis:
@@ -444,7 +448,8 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
                         ncpus: Union[int, None] = None,
                         path_connectivity_db: Union[str, bytes, os.PathLike, None] = None,
                         minimum_frequency: Union[int, None] = None,
-                        yield_smi_set: bool = True
+                        yield_smi_set: bool = True,
+                        isomeric_smiles: bool = False
                         ) -> Union[Sequence[list], None]:
     """
     Generate molecules of a given mass using chemical substructures and connectivity graphs. Can optionally take a
@@ -501,6 +506,8 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
 
     :param yield_smi_set: If True, yields a set of unique SMILEs string for each input molecule, else returns None.
 
+    :param isomeric_smiles:  If True, writes smiles with non-structural isomeric information.
+
     :return: For each input molecule, yields a set of unique SMILEs strings (unless
         `yield_smi_set = False`).
     """
@@ -547,7 +554,8 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
             out_mode="w",
             table_name=table_name,
             ncpus=ncpus,
-            clean=False
+            clean=False,
+            isomeric_smiles=isomeric_smiles
         )
 
         if yield_smi_set:
@@ -557,7 +565,7 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
 
 
 def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_db, path_substructure_db,
-          prescribed_mass, ppm, out_mode, ncpus, table_name, clean):
+          prescribed_mass, ppm, out_mode, ncpus, table_name, clean, isomeric_smiles):
     """
     Core function for generating molecules of a given mass using substructures and connectivity graphs. Can optionally
     take a "prescribed" fragment mass to further filter results; this can be used to incorporate MSn data. Final
@@ -602,6 +610,8 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
 
     :param clean: Whether to remove the temporary table of substructures, table_name`, after the method is complete.
 
+    :param isomeric_smiles:  If True, writes smiles with non-structural isomeric information.
+
     :return: Returns a set of unique SMILEs strings.
     """
 
@@ -671,7 +681,8 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
 
     with multiprocessing.Pool(processes=ncpus) as pool:  # send sets of substructures for building
         smi_lists = pool.map(
-            partial(substructure_combination_build, configs_iso=configs_iso, prescribed_structure=prescribed_mass is not None),
+            partial(substructure_combination_build, configs_iso=configs_iso,
+                    prescribed_structure=prescribed_mass is not None, isomeric_smiles=isomeric_smiles),
             substructure_subsets
         )
 
@@ -809,7 +820,7 @@ def build_from_subsets(exact_subset, mf, table_name, db):
     return substructure_subsets
 
 
-def substructure_combination_build(substructure_subset, configs_iso, prescribed_structure):
+def substructure_combination_build(substructure_subset, configs_iso, prescribed_structure, isomeric_smiles):
     """
     Final stage for building molecules; takes a combination of substructures (substructure_combination) and builds them
     according to graphs in the substructure database. May be run in parallel.
@@ -879,7 +890,7 @@ def substructure_combination_build(substructure_subset, configs_iso, prescribed_
                 continue
 
             try:  # append the canonical smiles of the final structure
-                smis.append(Chem.MolToSmiles(mol_out, isomericSmiles=False))
+                smis.append(Chem.MolToSmiles(mol_out, isomericSmiles=isomeric_smiles))
             except RuntimeError:
                 continue  # bad bond type violation
 
diff --git a/metaboblend/databases.py b/metaboblend/databases.py
index b2aa168..2436ecf 100644
--- a/metaboblend/databases.py
+++ b/metaboblend/databases.py
@@ -573,7 +573,7 @@ def close(self, clean=True):
         self.conn.close()
 
 
-def get_substructure(mol, idxs_edges_subgraph):
+def get_substructure(mol, idxs_edges_subgraph, isomeric_smiles=False):
     """
     Generates information for the substructure database from a reference molecule and the bond IDs of a substructure.
 
@@ -583,6 +583,8 @@ def get_substructure(mol, idxs_edges_subgraph):
         substructure (as returned by :py:meth:`metaboblend.databases.get_sgs`) or an integer representing the index
         of a single atom.
 
+    :param isomeric_smiles: If True, returns smiles with non-structural isomeric information.
+
     :return: A list of lists containing the libs of the substructures obtained by the query; the lib is a
         dictionary containing details about the substructure, in the format:
 
@@ -688,7 +690,7 @@ def get_substructure(mol, idxs_edges_subgraph):
     except:
         return
 
-    return {"smiles": Chem.MolToSmiles(mol_out, isomericSmiles=False),  # REORDERED ATOM INDEXES
+    return {"smiles": Chem.MolToSmiles(mol_out, isomericSmiles=isomeric_smiles),  # REORDERED ATOM INDEXES
             "mol": mol_out,
             "bond_types": bond_types,
             "degree_atoms": degree_atoms,
@@ -746,12 +748,14 @@ def calculate_exact_mass(mol, exact_mass_elements=None):
     return exact_mass
 
 
-def filter_records(records):
+def filter_records(records, isomeric_smiles=False):
     """
     Filters records generated by :py:meth:`parse_xml` to ensure they are compatible with the MetaboBlend workflow.
 
     :param records: A dictionary containing information about the molecule, as generated by :py:meth:`parse_xml`.
 
+    :param isomeric_smiles: If True, returns smiles with non-structural isomeric information.
+
     :return: Generates a dictionary containing key information extracted from the record.
 
         * "**HMDB_ID**": The HMDB ID of the molecule, in the format "HMDBXXXXXXX".
@@ -791,10 +795,9 @@ def filter_records(records):
             if len(atom_check) > 0:
                 continue
 
-            smiles_rdkit = Chem.MolToSmiles(mol, isomericSmiles=False)
-            smiles_rdkit_kek = Chem.MolToSmiles(mol, isomericSmiles=False, kekuleSmiles=True)
+            smiles_rdkit = Chem.MolToSmiles(mol, isomericSmiles=isomeric_smiles)
 
-            if "+" in smiles_rdkit_kek or "-" in smiles_rdkit_kek or "+" in smiles_rdkit or "-" in smiles_rdkit:
+            if "+" in smiles_rdkit or "-" in smiles_rdkit:
                 continue  # only neutral molecules are compatible
 
             els = get_elements(mol)
@@ -805,7 +808,7 @@ def filter_records(records):
                            'exact_mass': round(exact_mass, 6),
                            'smiles': record['smiles'],
                            'smiles_rdkit': smiles_rdkit,
-                           'smiles_rdkit_kek': smiles_rdkit_kek,
+                           'smiles_rdkit_kek': Chem.MolToSmiles(mol, isomericSmiles=isomeric_smiles, kekuleSmiles=True),
                            'C': els['C'],
                            'H': els['H'],
                            'N': els['N'],
@@ -950,7 +953,8 @@ def create_substructure_database(hmdb_paths: Union[str, bytes, os.PathLike],
                                  max_degree: Union[int, None] = None,
                                  max_atoms_available: Union[int, None] = None,
                                  method: str = "exhaustive",
-                                 substructures_only: bool = False) -> None:
+                                 substructures_only: bool = False,
+                                 isomeric_smiles: bool = False) -> None:
     """
     Creates a substructure database by fragmenting one or more input molecules. Combinations of
     substructures in this database are used to build new molecules. Fragmentation is carried out by selecting
@@ -993,6 +997,8 @@ def create_substructure_database(hmdb_paths: Union[str, bytes, os.PathLike],
 
     :param substructures_only: Whether to generate all tables or only the substructures table. Retains necessary
         information for building and reduces database size.
+
+    :param isomeric_smiles: If True, generates a database using smiles with non-structural isomeric information.
     """
 
     db = SubstructureDb(path_substructure_db)
@@ -1002,7 +1008,8 @@ def create_substructure_database(hmdb_paths: Union[str, bytes, os.PathLike],
     for hmdb_path in hmdb_paths:
         update_substructure_database(hmdb_path=hmdb_path, path_substructure_db=path_substructure_db, ha_min=ha_min,
                                      ha_max=ha_max, method=method, max_atoms_available=max_atoms_available,
-                                     max_degree=max_degree, substructures_only=substructures_only)
+                                     max_degree=max_degree, substructures_only=substructures_only,
+                                     isomeric_smiles=isomeric_smiles)
 
     db = SubstructureDb(path_substructure_db)
     db.create_indexes()
@@ -1017,7 +1024,8 @@ def update_substructure_database(hmdb_path: Union[str, bytes, os.PathLike, None]
                                  max_degree: Union[int, None] = None,
                                  method: str = "exhaustive",
                                  substructures_only: bool = False,
-                                 records: Union[Sequence[Dict], None] = None) -> None:
+                                 records: Union[Sequence[Dict], None] = None,
+                                 isomeric_smiles: bool = False) -> None:
     """
     Add entries to the substructure database by fragmenting a molecule or set of molecules. Combinations of
     substructures in this database are used to build new molecules. Fragmentation is carried out by selecting
@@ -1064,6 +1072,8 @@ def update_substructure_database(hmdb_path: Union[str, bytes, os.PathLike, None]
     :param records: Records of molecules to be fragmented. Must be a list containing dictionaries containing key
         information about the molecules, as generated by :py:meth:`metaboblend.databases.parse_xml`; if records
         is not supplied, the records will be obtained from the XML at `hmdb_path`.
+
+    :param isomeric_smiles: If True, generates a database using smiles with non-structural isomeric information.
     """
 
     conn = sqlite3.connect(path_substructure_db)
@@ -1075,7 +1085,7 @@ def update_substructure_database(hmdb_path: Union[str, bytes, os.PathLike, None]
     if ha_min is None:
         ha_min = 0
 
-    for record_dict in filter_records(records):
+    for record_dict in filter_records(records, isomeric_smiles=isomeric_smiles):
         if not substructures_only:
             cursor.execute("""INSERT OR IGNORE INTO compounds (
                                    hmdbid, 
@@ -1093,23 +1103,25 @@ def update_substructure_database(hmdb_path: Union[str, bytes, os.PathLike, None]
         # Returns a tuple of 2-tuples with bond IDs
         for sgs in get_sgs(record_dict=record_dict, n_min=ha_min-1, n_max=ha_max-1, method=method):
             for edge_idxs in sgs:
-                lib = get_substructure(record_dict["mol"], edge_idxs)  # convert bond IDs to substructure mol
+                lib = get_substructure(record_dict["mol"], edge_idxs, isomeric_smiles=isomeric_smiles)  # convert bond IDs to substructure mol
 
                 # insert substructure obtained from get_sgs
-                insert_substructure(lib, cursor, record_dict, substructures_only, max_atoms_available, max_degree)
+                insert_substructure(lib, cursor, record_dict, substructures_only, max_atoms_available, max_degree,
+                                    isomeric_smiles)
 
         if ha_min <= 1:
             for atom in record_dict["mol"].GetAtoms():
-                lib = get_substructure(record_dict["mol"], atom.GetIdx())
+                lib = get_substructure(record_dict["mol"], atom.GetIdx(), isomeric_smiles=isomeric_smiles)
 
                 # insert single atom substructures
-                insert_substructure(lib, cursor, record_dict, substructures_only, max_atoms_available, max_degree)
+                insert_substructure(lib, cursor, record_dict, substructures_only, max_atoms_available, max_degree,
+                                    isomeric_smiles)
 
     conn.commit()
     conn.close()
 
 
-def insert_substructure(lib, cursor, record_dict, substructures_only, max_atoms_available, max_degree):
+def insert_substructure(lib, cursor, record_dict, substructures_only, max_atoms_available, max_degree, isomeric_smiles):
     """
     Converts the details of a single substructure into an entry in a substructure database. See
     :py:meth:`update_substructure_database`.
@@ -1158,6 +1170,8 @@ def insert_substructure(lib, cursor, record_dict, substructures_only, max_atoms_
         2 = double, etc.). Maximum degree is also limited by the extensivity of the supplied connectivity database. For
         instance, a substructure that has 3 `atoms_available`, each of their bond types being single bonds, would have
         a total degree of 3.
+
+    :param isomeric_smiles: If True, generates database entries using smiles with non-structural isomeric information.
     """
 
     if lib is None:
@@ -1171,7 +1185,7 @@ def insert_substructure(lib, cursor, record_dict, substructures_only, max_atoms_
         if lib["valence"] > max_degree:
             return
 
-    smiles_rdkit = Chem.MolToSmiles(lib["mol"], isomericSmiles=False)  # canonical rdkit smiles
+    smiles_rdkit = Chem.MolToSmiles(lib["mol"], isomericSmiles=isomeric_smiles)  # canonical rdkit smiles
 
     exact_mass = calculate_exact_mass(lib["mol"])
     els = get_elements(lib["mol"])
diff --git a/tests/test_databases.py b/tests/test_databases.py
index 1cd59c6..a997642 100644
--- a/tests/test_databases.py
+++ b/tests/test_databases.py
@@ -83,7 +83,7 @@ def test_filter_records(self):
         with open(self.to_test_data("test_hmdbs.dictionary"), "rb") as test_hmdbs:
             filtered_records = pickle.load(test_hmdbs)
 
-        record_gen = filter_records(parsed_records.values())
+        record_gen = filter_records(parsed_records.values(), isomeric_smiles=True)
         test_filtered_records = {}
         for record in record_gen:
             del record["mol"]
@@ -150,7 +150,7 @@ def test_get_substructure(self):
 
         for edges in [[(0, 1, 2, 22, 3, 16, 17, 18, 19, 20, 21), (0, 1, 2, 22, 3, 4, 16, 17, 18, 19, 20)]]:
             for i, edge_idx in enumerate(edges):
-                lib = get_substructure(mol, edge_idx)
+                lib = get_substructure(mol, edge_idx, isomeric_smiles=True)
                 del lib["mol"]
                 self.assertEqual(lib, libs[i])
 
@@ -188,8 +188,8 @@ def test_calculate_exact_mass(self):
     def test_create_substructure_database(self):
         records = [self.to_test_data(r + ".xml") for r in ["HMDB0000073", "HMDB0000122", "HMDB0000158", "HMDB0000186"]]
 
-        create_substructure_database(records,
-                                     self.to_test_results("test_db.sqlite"), 4, 8, method="exhaustive")
+        create_substructure_database(records, self.to_test_results("test_db.sqlite"), 4, 8, method="exhaustive",
+                                     isomeric_smiles=True)
 
         test_db = sqlite3.connect(self.to_test_results("test_db.sqlite"))
         test_db_cursor = test_db.cursor()
@@ -259,7 +259,8 @@ def test_update_substructure_database(self):  # requires create_compound_databas
             record = self.to_test_data(record + ".xml")
 
             update_substructure_database(self.to_test_data(record),
-                                         self.to_test_results("test_db.sqlite"), 4, 8, method="exhaustive")
+                                         self.to_test_results("test_db.sqlite"), 4, 8,
+                                         method="exhaustive", isomeric_smiles=True)
 
         test_db = sqlite3.connect(self.to_test_results("test_db.sqlite"))
         test_db_cursor = test_db.cursor()
@@ -328,7 +329,8 @@ def test_update_substructure_database(self):  # requires create_compound_databas
             record = self.to_test_data(record + ".xml")
 
             update_substructure_database(self.to_test_data(record),
-                                         self.to_test_results("test_db.sqlite"), 1, 1, method="exhaustive")
+                                         self.to_test_results("test_db.sqlite"), 1, 1,
+                                         method="exhaustive", isomeric_smiles=True)
 
         test_db = sqlite3.connect(self.to_test_results("test_db.sqlite"))
         test_db_cursor = test_db.cursor()

From 4726b3aa285f12984538c55e84b204c28b4d10a0 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 19 Sep 2020 14:07:17 +0100
Subject: [PATCH 04/35] Update tests

---
 metaboblend/build_structures.py     |  2 +-
 tests/test_build_structures.py      | 45 +++++++++++++++++++----------
 tests/test_substructure_database.py | 12 --------
 3 files changed, 30 insertions(+), 29 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index f3707a3..c579c1f 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -682,7 +682,7 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
     with multiprocessing.Pool(processes=ncpus) as pool:  # send sets of substructures for building
         smi_lists = pool.map(
             partial(substructure_combination_build, configs_iso=configs_iso,
-                    prescribed_structure=prescribed_mass is not None, isomeric_smiles=isomeric_smiles),
+                    prescribed_structure=prescribed_mass, isomeric_smiles=isomeric_smiles),
             substructure_subsets
         )
 
diff --git a/tests/test_build_structures.py b/tests/test_build_structures.py
index 5b4ad77..1cb02f0 100644
--- a/tests/test_build_structures.py
+++ b/tests/test_build_structures.py
@@ -71,7 +71,8 @@ def test_build(self):  # core - all other build functions rely on
                     path_smi_out=self.to_test_results(record_dict["HMDB_ID"] + ".smi"), max_n_substructures=3,
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"), clean=True,
-                    prescribed_mass=None, ppm=None, out_mode="w", ncpus=None, table_name="substructures"
+                    prescribed_mass=None, ppm=None, out_mode="w", ncpus=None, table_name="substructures",
+                    isomeric_smiles=True
                 )
 
                 j = 0
@@ -98,7 +99,8 @@ def test_build(self):  # core - all other build functions rely on
                     prescribed_mass=fragments[i], ppm=15, clean=True,
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    out_mode="w", ncpus=None, table_name="substructures"
+                    out_mode="w", ncpus=None, table_name="substructures",
+                    isomeric_smiles=True
                 )
 
                 j = 0
@@ -124,12 +126,13 @@ def test_substructure_combination_build(self):
         ec_products = [((4, 5, 0, 0, 0, 0), (4, 6, 1, 2, 0, 0)),
                        ((5, 5, 0, 2, 0, 0), (3, 6, 1, 0, 0, 0)),
                        ((2, 4, 0, 2, 0, 0), (4, 8, 0, 4, 0, 0))]
-        configs_iso = db.k_configs(True)
+        configs_iso = db.k_configs()
         lens = [0, 1, 60]
 
         for i, ec_product in enumerate(ec_products):
             substructure_subset = db.select_substructures(ec_product, "substructures")
-            smis = substructure_combination_build(substructure_subset, configs_iso)
+            smis = substructure_combination_build(substructure_subset, configs_iso,
+                                                  prescribed_structure=False, isomeric_smiles=True)
 
             self.assertEqual(len(smis), lens[i])
 
@@ -187,7 +190,8 @@ def test_generate_structures(self):  # tests vs build
                     path_smi_out=self.to_test_results(),
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    minimum_frequency=None, yield_smi_set=True
+                    minimum_frequency=None, yield_smi_set=True,
+                    isomeric_smiles=True
                 ))
 
                 build_smis = build(
@@ -197,7 +201,8 @@ def test_generate_structures(self):  # tests vs build
                     path_smi_out=self.to_test_results(record_dict["HMDB_ID"] + "_build.smi"),
                     max_n_substructures=3, path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"), clean=True,
-                    prescribed_mass=None, ppm=None, out_mode="w", ncpus=None, table_name="substructures"
+                    prescribed_mass=None, ppm=None, out_mode="w", ncpus=None, table_name="substructures",
+                    isomeric_smiles=True
                 )
 
                 unique_smis = set()
@@ -219,7 +224,8 @@ def test_generate_structures(self):  # tests vs build
                     path_smi_out=self.to_test_results(),
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    minimum_frequency=None, yield_smi_set=True
+                    minimum_frequency=None, yield_smi_set=True,
+                    isomeric_smiles=True
                 ))
 
                 build_smis = build(
@@ -230,7 +236,8 @@ def test_generate_structures(self):  # tests vs build
                     prescribed_mass=fragments[i], ppm=0,
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    out_mode="w", ncpus=None, table_name="substructures", clean=True
+                    out_mode="w", ncpus=None, table_name="substructures", clean=True,
+                    isomeric_smiles=True
                 )
 
                 unique_smis = set()
@@ -254,7 +261,8 @@ def test_generate_structures(self):  # tests vs build
                 path_smi_out=self.to_test_results(),
                 path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                 path_substructure_db=self.to_test_data("substructures.sqlite"),
-                minimum_frequency=None, yield_smi_set=True
+                minimum_frequency=None, yield_smi_set=True,
+                isomeric_smiles=True
             ))
 
             for i, record_dict in enumerate(record_dicts.values()):
@@ -265,7 +273,8 @@ def test_generate_structures(self):  # tests vs build
                     path_smi_out=self.to_test_results(record_dict["HMDB_ID"] + "_build.smi"),
                     max_n_substructures=3, path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"), clean=True,
-                    prescribed_mass=None, ppm=None, out_mode="w", ncpus=None, table_name="substructures"
+                    prescribed_mass=None, ppm=None, out_mode="w", ncpus=None, table_name="substructures",
+                    isomeric_smiles=True
                 )
 
                 unique_smis = set()
@@ -308,9 +317,12 @@ def test_annotate_msn(self):  # tests vs build_msn
                     path_smi_out=self.to_test_results("annotate"),
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    minimum_frequency=None, yield_smi_dict=True, write_fragment_smis=True
+                    minimum_frequency=None, yield_smi_dict=True, write_fragment_smis=True,
+                    isomeric_smiles=True
                 ))
 
+                returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
+
                 peak_smis = set()
                 for prescribed_mass in fragments:
                     with open(self.to_test_results("annotate", "1_" + record_dict["HMDB_ID"],
@@ -326,17 +338,17 @@ def test_annotate_msn(self):  # tests vs build_msn
                             csv_output_smis.add(line.split()[0])
 
                 self.assertEqual(peak_smis, csv_output_smis)
-                self.assertEqual(peak_smis, set(returned_smis[0].keys()))
+                self.assertEqual(peak_smis, set(returned_smis.keys()))
 
                 self.assertEqual(len(peak_smis), overall_lens[i])
 
-                self.assertEqual(len([freq for freq in set(returned_smis[0].values()) if freq > 1]), freqs[i])
+                self.assertEqual(len([freq for freq in set(returned_smis.values()) if freq > 1]), freqs[i])
 
                 if smis[i] is not None:
                     self.assertEqual(peak_smis, smis[i])
 
                 if i == 0:
-                    self.assertEqual(returned_smis[0]['NCCc1ccc(O)c(O)c1'], 2)
+                    self.assertEqual(returned_smis['NCCc1ccc(O)c(O)c1'], 3)
 
             ms_data = {}
             for i, record_dict in enumerate(record_dicts.values()):
@@ -354,7 +366,8 @@ def test_annotate_msn(self):  # tests vs build_msn
                 path_smi_out=self.to_test_results("annotate_multi"),
                 path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                 path_substructure_db=self.to_test_data("substructures.sqlite"),
-                minimum_frequency=None, yield_smi_dict=True
+                minimum_frequency=None, yield_smi_dict=True,
+                isomeric_smiles=True
             ))
 
             for i, record_dict in enumerate(record_dicts.values()):
@@ -365,7 +378,7 @@ def test_annotate_msn(self):  # tests vs build_msn
                     for line in smi_out:
                         unique_smis.add(line.split()[0].split(",")[0])
 
-                self.assertEqual(unique_smis, set(returned_smi_list[i].keys()))
+                self.assertEqual(unique_smis, set(returned_smi_list[i][record_dict["HMDB_ID"]].keys()))
                 self.assertEqual(len(unique_smis), overall_lens[i])
 
         db.close()
diff --git a/tests/test_substructure_database.py b/tests/test_substructure_database.py
index 908e201..a950e1d 100644
--- a/tests/test_substructure_database.py
+++ b/tests/test_substructure_database.py
@@ -193,18 +193,6 @@ def test_k_configs(self):
                           ((0, 3), (0, 5), (1, 2), (1, 4), (2, 4), (3, 5)),
                           ((0, 3), (0, 4), (1, 2), (1, 5), (2, 5), (3, 4))])
 
-        k_configs = db.k_configs(True)
-        self.assertEqual(len(k_configs), 10)
-        self.assertEqual(k_configs['((1,), (1,))'], [((0, 1),)])
-        self.assertTrue('((2, 2), (2, 2), (2, 2))' not in k_configs.keys())
-        self.assertEqual(k_configs['((2, 2), (1, 1), (1, 1))'],
-                         [((0, 2), (0, 4), (1, 3), (1, 5)),
-                          ((0, 2), (0, 3), (1, 4), (1, 5)),
-                          ((0, 2), (0, 5), (1, 3), (1, 4)),
-                          ((0, 4), (0, 5), (1, 2), (1, 3)),
-                          ((0, 3), (0, 5), (1, 2), (1, 4)),
-                          ((0, 3), (0, 4), (1, 2), (1, 5))])
-
         db.close()
 
     def test_select_substructures(self):

From dd2013b1ee07a535231bb6c1469116a49d909b04 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 19 Sep 2020 23:41:18 +0100
Subject: [PATCH 05/35] Correct return type hints

---
 metaboblend/build_structures.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index c579c1f..decc5e0 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -292,7 +292,8 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                  minimum_frequency: Union[int, None] = None,
                  hydrogenation_allowance: int = 2,
                  yield_smi_dict: bool = True,
-                 isomeric_smiles: bool = False) -> Union[Sequence[Dict[str, int]], None]:
+                 isomeric_smiles: bool = False
+                 ) -> Dict[str, Sequence[Dict[str, int]]]:
     """
     Generate molecules of a given mass using chemical substructures, connectivity graphs and spectral trees or
     fragmentation spectra. Final structures and rankings are yielded by the function as a dictionary and/or written in
@@ -450,7 +451,7 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
                         minimum_frequency: Union[int, None] = None,
                         yield_smi_set: bool = True,
                         isomeric_smiles: bool = False
-                        ) -> Union[Sequence[list], None]:
+                        ) -> Dict[str, Sequence[list]]:
     """
     Generate molecules of a given mass using chemical substructures and connectivity graphs. Can optionally take a
     "prescribed" fragment mass to further filter results. Final structures are returned as a list and/or written in
@@ -542,7 +543,7 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
         else:
             id_path_smi_out = None
 
-        smi_list = build(
+        smi_set = build(
             mf=ms_data[ms_id]["mf"],
             exact_mass=ms_data[ms_id]["exact_mass"],
             max_n_substructures=max_n_substructures,
@@ -559,7 +560,7 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
         )
 
         if yield_smi_set:
-            yield smi_list
+            yield {ms_id: smi_set}
 
     db.close()
 
@@ -743,7 +744,7 @@ def gen_subs_table(db, ha_min, ha_max, max_degree, max_atoms_available, max_mass
                                     FROM hmdbid_substructures 
                                     GROUP BY smiles 
                                     HAVING COUNT(*) >= {})
-                            """.format(minimum_frequency,)
+                         """.format(minimum_frequency,)
 
     if ha_min is None:
         ha_min_statement = ""
@@ -759,7 +760,6 @@ def gen_subs_table(db, ha_min, ha_max, max_degree, max_atoms_available, max_mass
 
     db.cursor.execute("""CREATE TABLE {} AS
                              SELECT * FROM substructures WHERE
-                                 
                                  atoms_available <= {} AND
                                  valence <= {} AND
                                  exact_mass__1 < {}{}{}{}

From 071e9a2f47ccb335d7e7d30b4984cde13c9b05c4 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 19 Sep 2020 23:41:42 +0100
Subject: [PATCH 06/35] Implement SQLITE3 annotate_msn results database

---
 metaboblend/build_structures.py | 207 ++++++++++++++++++++++++++------
 tests/test_build_structures.py  |  49 ++------
 2 files changed, 184 insertions(+), 72 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index decc5e0..360f3da 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -26,6 +26,7 @@
 from functools import partial
 import networkx as nx
 import numpy
+import sqlite3
 from operator import itemgetter
 from typing import Sequence, Dict, Union
 
@@ -277,18 +278,173 @@ def add_bonds(mols, edges, atoms_available, bond_types):
     return mol_edit
 
 
+class ResultsDb:
+    """
+    Methods for interacting with the SQLITE3 results database, as created by
+    :py:meth:`metaboblend.build_structures.annotate_msn`.
+
+    :param path_results_db: Path to the results database.
+    """
+
+    def __init__(self, path_results_db):
+        """Constructor method."""
+
+        self.path_results_db = path_results_db
+
+        self.conn = None
+        self.cursor = None
+
+    def connect(self):
+        """Connects to the results database."""
+
+        self.conn = sqlite3.connect(self.path_results_db)
+        self.cursor = self.conn.cursor()
+
+    def create_results_table(self):
+        """Generates a new results database."""
+
+        if os.path.exists(self.path_results_db):
+            os.remove(self.path_results_db)
+
+        self.connect()
+
+        self.cursor.execute("""CREATE TABLE results (
+                                   ms_id TEXT PRIMARY KEY,
+                                   exact_mass NUMERIC,
+                                   mc TEXT,
+                                   ppm INTEGER,
+                                   ha_min INTEGER,
+                                   ha_max INTEGER,
+                                   max_atoms_available INTEGER,
+                                   max_degree INTEGER,
+                                   max_n_substructures INTEGER,
+                                   hydrogenation_allowance INTEGER,
+                                   isomeric_smiles INTEGER,
+                                   neutral_fragments TEXT)""")
+
+        self.conn.commit()
+
+    def add_ms(self, msn_data, parameters):
+        """
+        Add entries to the results table and set up test-specific tables.
+
+        :param msn_data: Dictionary in the form
+            `msn_data[id] = {mf: [C, H, N, O, P, S], exact_mass: float, fragment_masses=[]}`. id represents a unique
+            identifier for a given spectral tree or fragmentation spectrum, mf is a list of integers referring to the
+            molecular formula of the structure of interest, exact_mass is the mass of this molecular formula to >=4d.p.
+            and fragment_masses are neutral fragment masses generated by this structure used to inform candidate
+            scoring. See :py:meth:`metaboblend.build_structures.annotate_msn`.
+
+        :param parameters: List of parameters, in the form: [ppm, ha_min, ha_max, max_atoms_available, max_degree,
+            max_n_substructures, hydrogenation_allowance, isomeric_smiles]. See
+            :py:meth:`metaboblend.build_structures.annotate_msn`.
+        """
+
+        for i, parameter in enumerate(parameters):
+            if parameter is None:
+                parameters[i] = "NULL"
+            elif isinstance(parameter, bool):
+                parameters[i] = int(parameter)
+
+        for ms_id in msn_data.keys():
+
+            self.cursor.execute("""INSERT INTO results (
+                                       ms_id,
+                                       exact_mass,
+                                       mc,
+                                       ppm,
+                                       ha_min,
+                                       ha_max,
+                                       max_atoms_available,
+                                       max_degree,
+                                       max_n_substructures,
+                                       hydrogenation_allowance,
+                                       isomeric_smiles,
+                                       neutral_fragments
+                                   ) VALUES ('{}', {}, '{}', {}, '{}')""".format(
+                                       ms_id,
+                                       msn_data[ms_id]["exact_mass"],
+                                       msn_data[ms_id]["mf"],
+                                       ", ".join([str(p) for p in parameters]),
+                                       str(msn_data[ms_id]["fragment_masses"])
+                                   ))
+
+            fragment_statement = "'" + ",\n'".join([str(mass) + "' INTEGER DEFAULT 0" for mass in msn_data[ms_id]["fragment_masses"]])
+            self.cursor.execute("""CREATE TABLE {} (
+                                       smiles TEXT PRIMARY KEY,
+                                       {},
+                                       peak_sum INTEGER DEFAULT 0)""".format(ms_id, fragment_statement))
+
+        self.conn.commit()
+
+    def add_smis(self, ms_id, smis, fragment_mass):
+        """
+        Record which smiles were generated for a given fragment mass.
+
+        :param ms_id: Unique identifer for the annotation of a single metabolite.
+
+        :param smis: The smiles generated by the annotation of a single peak for a single metabolite.
+
+        :param fragment_mass: The neutral fragment mass that has been annotated.
+        """
+
+        self.cursor.executemany("INSERT OR IGNORE INTO {} (smiles) VALUES (?)".format(ms_id), [[smi] for smi in smis])
+
+        self.cursor.execute("UPDATE {} SET '{}' = 1 WHERE smiles IN ('{}')".format(ms_id,
+                                                                                   str(fragment_mass),
+                                                                                   "', '".join(smis)))
+
+        self.conn.commit()
+
+    def calc_scores(self, ms_id, fragment_masses):
+        """
+        Calculate number of peaks for which a given structure was generated.
+
+        :param ms_id: Unique identifer for the annotation of a single metabolite.
+
+        :param fragment_masses: The neutral fragment masses that have been annotated.
+        """
+
+        self.cursor.execute("UPDATE {} SET peak_sum = [{}]".format(
+            ms_id,
+            "] + [".join([str(fragment_mass) for fragment_mass in fragment_masses])
+        ))
+
+        self.conn.commit()
+
+    def structure_frequency(self, ms_id):
+        """
+        Generate a dictionary of results with structure frequencies.
+
+        :param ms_id: Unique identifer for the annotation of a single metabolite.
+
+        :return: A dictionary with smiles as keys and the number of peaks for which the smiles were generated as
+        values
+        """
+
+        structure_frequencies = {}
+        for smiles, peak_sum in self.cursor.execute("""SELECT smiles, peak_sum FROM {}""".format(ms_id)):
+            structure_frequencies[smiles] = peak_sum
+
+        return structure_frequencies
+
+    def close(self):
+        """Close the connection to the SQLITE3 database"""
+
+        self.conn.close()
+
+
 def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                  path_substructure_db: Union[str, bytes, os.PathLike],
+                 path_sql_out: Union[str, bytes, os.PathLike] = "metaboblend_results.sqlite",
                  ppm: int = 5,
                  ha_min: Union[int, None] = None,
                  ha_max: Union[int, None] = None,
                  max_atoms_available: int = 2,
                  max_degree: int = 6,
                  max_n_substructures: int = 3,
-                 path_smi_out: Union[str, bytes, os.PathLike, None] = None,
                  path_connectivity_db: Union[str, bytes, os.PathLike, None] = None,
                  ncpus: Union[int, None] = None,
-                 write_fragment_smis: bool = False,
                  minimum_frequency: Union[int, None] = None,
                  hydrogenation_allowance: int = 2,
                  yield_smi_dict: bool = True,
@@ -309,6 +465,8 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
     :param path_substructure_db: The path to the SQLite 3 substructure database, as generated by
         :py:meth:`metaboblend.databases.SubstructureDb`.
 
+    :param path_sql_out: The path at which an SQLite database will be generated to store results.
+
     :param ppm: The maximal tolerated m/z deviation (in parts per million) of the mass of substructures from the
         supplied `fragment_masses`.
 
@@ -332,11 +490,6 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
     :param max_n_substructures: The maximum number of substructures to be used for building molecules. The max number
         of substructures is also limited by the extensivity of the supplied connectivity database.
 
-    :param path_smi_out: The directory to which unique smile strings should be written representing the final
-        structures generated and their respective scores; this is a csv file unique SMILEs strings of structures built
-        and the number of `prescribed_masses` by which each structure was generated. If None, no files are written.
-        See `write_fragment_smis` should you wish to inspect the SMILEs generated by individual peaks.
-
     :param path_connectivity_db: The path to the SQLite 3 connectivity database, as generated by
         :py:meth:`metaboblend.databases.create_isomorphism_database`. If the path is None, the default connectivity
         database bundled with MetaboBlend will be used.
@@ -346,10 +499,6 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
     :param minimum_frequency: The minimum frequency of substructures in table_name; e.g. substructures have a frequency
         of 1 if they are unique. Defaults to None, in which case this filtering method is not applied.
 
-    :param write_fragment_smis: Whether to write smiles to a file for each fragment. Requires `path_smi_out` to be
-        specified. If `True`, a new directory is generated for each input spectral tree and the results for each
-        fragment are stored as a text file that contains unique smiles strings.
-
     :param hydrogenation_allowance: In order to represent re-arrangement events (the movement of hydrogens), in
         addition to attempting to build from substructures in prescribed_masses, we also attempt to build from
         `fragment_masses +- hydrogenation_allowance`. E.g. if `prescribed_masses = [141.5938]` and
@@ -373,6 +522,8 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
         path_connectivity_db = os.path.join(os.path.realpath(os.path.dirname(__file__)), "data", "connectivity.sqlite")
 
     db = SubstructureDb(path_substructure_db, path_connectivity_db)
+    results_db = ResultsDb(path_sql_out)
+    results_db.create_results_table()
 
     # prepare temporary table here - will only be generated once in case of multiple input
     table_name = gen_subs_table(
@@ -385,57 +536,41 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
         max_mass=round(max([msn_data[ms_id]["exact_mass"] for ms_id in msn_data.keys()]))
     )
 
-    for i, ms_id in enumerate(msn_data.keys()):
-        if path_smi_out is not None and write_fragment_smis:
-            path_smi_out_subdir = os.path.join(path_smi_out, str(i+1) + "_" + ms_id)
-            os.mkdir(path_smi_out_subdir)
-        else:
-            path_smi_out_subdir = None
+    results_db.add_ms(msn_data, [ppm, ha_min, ha_max, max_atoms_available, max_degree, max_n_substructures,
+                                 hydrogenation_allowance, isomeric_smiles])
 
-        structure_frequency = {}  # map smiles to how many separate masses generated them
-        msn_data[ms_id]["fragment_masses"].sort(reverse=True)
+    for i, ms_id in enumerate(msn_data.keys()):
 
         for prescribed_mass in msn_data[ms_id]["fragment_masses"]:
-            if path_smi_out_subdir is not None and write_fragment_smis:
-                id_path_smi_out = os.path.join(path_smi_out_subdir, str(round(prescribed_mass, 4)) + ".smi")
-                open(id_path_smi_out, "w").close()
-            else:
-                id_path_smi_out = None
-
             fragment_smis = set()
-
             for j in range(0 - hydrogenation_allowance, hydrogenation_allowance + 1):
                 hydrogenated_prescribed_mass = prescribed_mass + (j * 1.007825)  # consider re-arrangements
 
                 fragment_smis.update(build(
                     mf=msn_data[ms_id]["mf"],
                     exact_mass=msn_data[ms_id]["exact_mass"],
-                    path_smi_out=id_path_smi_out,
                     max_n_substructures=max_n_substructures,
+                    path_smi_out=None,
                     path_connectivity_db=path_connectivity_db,
                     path_substructure_db=path_substructure_db,
                     prescribed_mass=hydrogenated_prescribed_mass,
                     ppm=ppm,
-                    out_mode="a",
+                    out_mode=None,
                     table_name=table_name,
                     ncpus=ncpus,
                     clean=False,
                     isomeric_smiles=isomeric_smiles
                 ))
 
-            for smi in fragment_smis:
-                structure_frequency[smi] = structure_frequency.get(smi, 0) + 1
+            results_db.add_smis(ms_id, fragment_smis, prescribed_mass)
 
-        # write structure_frequency dict as csv
-        if path_smi_out is not None:
-            with open(os.path.join(path_smi_out, ms_id + "_frequency.csv"), "w") as freq_out:
-                freq_out.writelines(
-                    [k + "," + str(i) + "\n" for k, i in zip(structure_frequency.keys(), structure_frequency.values())])
+        results_db.calc_scores(ms_id, msn_data[ms_id]["fragment_masses"])
 
         if yield_smi_dict:
-            yield {ms_id: structure_frequency}
+            yield {ms_id: results_db.structure_frequency(ms_id)}
 
     db.close()
+    results_db.close()
 
 
 def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
diff --git a/tests/test_build_structures.py b/tests/test_build_structures.py
index 1cb02f0..87cc2ab 100644
--- a/tests/test_build_structures.py
+++ b/tests/test_build_structures.py
@@ -194,6 +194,8 @@ def test_generate_structures(self):  # tests vs build
                     isomeric_smiles=True
                 ))
 
+                returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
+
                 build_smis = build(
                     mf=[record_dict["C"], record_dict["H"], record_dict["N"],
                         record_dict["O"], record_dict["P"], record_dict["S"]],
@@ -210,7 +212,7 @@ def test_generate_structures(self):  # tests vs build
                     for line in smi_out:
                         unique_smis.add(line.split()[0])
 
-                self.assertEqual(unique_smis, returned_smis[0])
+                self.assertEqual(unique_smis, returned_smis)
                 self.assertEqual(unique_smis, build_smis)
 
                 ms_data = {record_dict["HMDB_ID"]: {"mf": [record_dict["C"], record_dict["H"], record_dict["N"],
@@ -228,6 +230,8 @@ def test_generate_structures(self):  # tests vs build
                     isomeric_smiles=True
                 ))
 
+                returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
+
                 build_smis = build(
                     mf=[record_dict["C"], record_dict["H"], record_dict["N"],
                         record_dict["O"], record_dict["P"], record_dict["S"]],
@@ -245,7 +249,7 @@ def test_generate_structures(self):  # tests vs build
                     for line in smi_out:
                         unique_smis.add(line.split()[0])
 
-                self.assertEqual(unique_smis, returned_smis[0])
+                self.assertEqual(unique_smis, returned_smis)
                 self.assertEqual(unique_smis, build_smis)
 
             ms_data = {}
@@ -255,6 +259,7 @@ def test_generate_structures(self):  # tests vs build
                                                           record_dict["O"], record_dict["P"], record_dict["S"]],
                                                    "exact_mass": record_dict["exact_mass"],
                                                    "prescribed_masses": None}
+
             # test building with multiple inputs
             returned_smi_list = list(generate_structures(
                 ms_data, max_degree=6, max_atoms_available=2, max_n_substructures=3,
@@ -282,7 +287,7 @@ def test_generate_structures(self):  # tests vs build
                     for line in smi_out:
                         unique_smis.add(line.split()[0])
 
-                self.assertEqual(unique_smis, returned_smi_list[i])
+                self.assertEqual(unique_smis, returned_smi_list[i][record_dict["HMDB_ID"]])
                 self.assertEqual(unique_smis, build_smis)
 
         db.close()
@@ -314,38 +319,18 @@ def test_annotate_msn(self):  # tests vs build_msn
                 # test standard building
                 returned_smis = list(annotate_msn(
                     ms_data, max_degree=6, max_atoms_available=2, max_n_substructures=3,
-                    path_smi_out=self.to_test_results("annotate"),
+                    path_sql_out=self.to_test_results("results.sqlite"),
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    minimum_frequency=None, yield_smi_dict=True, write_fragment_smis=True,
-                    isomeric_smiles=True
+                    minimum_frequency=None, yield_smi_dict=True, isomeric_smiles=True
                 ))
 
                 returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
 
-                peak_smis = set()
-                for prescribed_mass in fragments:
-                    with open(self.to_test_results("annotate", "1_" + record_dict["HMDB_ID"],
-                                                   str(round(prescribed_mass, 4))) + ".smi", "r") as smi_out:
-                        for line in smi_out:
-                            peak_smis.add(line.split()[0])
-
-                csv_output_smis = set()
-                for prescribed_mass in fragments:
-                    with open(self.to_test_results("annotate", "1_" + record_dict["HMDB_ID"],
-                                                   str(round(prescribed_mass, 4))) + ".smi", "r") as smi_out:
-                        for line in smi_out:
-                            csv_output_smis.add(line.split()[0])
-
-                self.assertEqual(peak_smis, csv_output_smis)
-                self.assertEqual(peak_smis, set(returned_smis.keys()))
-
-                self.assertEqual(len(peak_smis), overall_lens[i])
-
                 self.assertEqual(len([freq for freq in set(returned_smis.values()) if freq > 1]), freqs[i])
 
                 if smis[i] is not None:
-                    self.assertEqual(peak_smis, smis[i])
+                    self.assertEqual(set(returned_smis.keys()), smis[i])
 
                 if i == 0:
                     self.assertEqual(returned_smis['NCCc1ccc(O)c(O)c1'], 3)
@@ -363,7 +348,7 @@ def test_annotate_msn(self):  # tests vs build_msn
             # test building with multiple inputs
             returned_smi_list = list(annotate_msn(
                 ms_data, max_degree=6, max_atoms_available=2, max_n_substructures=3,
-                path_smi_out=self.to_test_results("annotate_multi"),
+                path_sql_out=self.to_test_results("annotate_multi", "results.sqlite"),
                 path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                 path_substructure_db=self.to_test_data("substructures.sqlite"),
                 minimum_frequency=None, yield_smi_dict=True,
@@ -371,15 +356,7 @@ def test_annotate_msn(self):  # tests vs build_msn
             ))
 
             for i, record_dict in enumerate(record_dicts.values()):
-                unique_smis = set()
-
-                with open(self.to_test_results("annotate_multi",
-                                               record_dict["HMDB_ID"] + "_frequency.csv"), "r") as smi_out:
-                    for line in smi_out:
-                        unique_smis.add(line.split()[0].split(",")[0])
-
-                self.assertEqual(unique_smis, set(returned_smi_list[i][record_dict["HMDB_ID"]].keys()))
-                self.assertEqual(len(unique_smis), overall_lens[i])
+                self.assertEqual(len(set(returned_smi_list[i][record_dict["HMDB_ID"]].keys())), overall_lens[i])
 
         db.close()
 

From 4d52c4002de94c9557c0851b78c715ac9a566d34 Mon Sep 17 00:00:00 2001
From: Jack Gisby <jackgisby@gmail.com>
Date: Sat, 21 Nov 2020 22:25:14 +0000
Subject: [PATCH 07/35] Add msn option to ResultsDb

---
 metaboblend/build_structures.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 360f3da..5ccdf74 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -32,7 +32,7 @@
 
 from rdkit import Chem
 
-from .databases import SubstructureDb
+from .databases import SubstructureDb, get_elements, calculate_exact_mass
 
 
 def find_path(mass_list, sum_matrix, n, mass, max_subset_length, path=[]):
@@ -79,9 +79,9 @@ def subset_sum(mass_list, mass, max_subset_length=3):
     """
     Dynamic programming implementation of subset sum. Note that, whilst this algorithm is pseudo-polynomial, the
     backtracking algorithm for obtaining all possible subsets has exponential complexity and so remains unsuitable
-    for large input values.  This does, however, tend to perform a lot better than non-sum_matrix implementations, as we're
-    no longer doing sums multiple times and we've cut down the operations performed during the exponential portion of
-    the method.
+    for large input values.  This does, however, tend to perform a lot better than non-sum_matrix implementations, as
+    we're no longer doing sums multiple times and we've cut down the operations performed during the exponential portion
+    of the method.
 
     :param mass_list: A list of masses from which to identify subsets.
 
@@ -121,8 +121,8 @@ def subset_sum(mass_list, mass, max_subset_length=3):
 def combine_mfs(precise_mass_grp, db, table_name, accuracy):
     """
     A wrapper for :py:meth:`metaboblend.databases.select_ecs` that instead takes a group of subsets, as generated by
-    the second stage of :py:meth:`metaboblend.build_structres.subset_sum` in
-    :py:meth:`metaboblend.build_structres.build`.
+    the second stage of :py:meth:`metaboblend.build_structures.subset_sum` in
+    :py:meth:`metaboblend.build_structures.build`.
 
     :param precise_mass_grp: A list containing the masses of substructures identified by subset_sum.
 
@@ -286,10 +286,11 @@ class ResultsDb:
     :param path_results_db: Path to the results database.
     """
 
-    def __init__(self, path_results_db):
+    def __init__(self, path_results_db, msn=True):
         """Constructor method."""
 
         self.path_results_db = path_results_db
+        self.msn = msn
 
         self.conn = None
         self.cursor = None

From 918b7b0d6389efaa824dc47c660db38f1e2f0477 Mon Sep 17 00:00:00 2001
From: Jack Gisby <jackgisby@gmail.com>
Date: Sat, 21 Nov 2020 22:25:46 +0000
Subject: [PATCH 08/35] Re-structure results db tables

---
 metaboblend/build_structures.py | 45 +++++++++++++++++++++++++++++----
 1 file changed, 40 insertions(+), 5 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 5ccdf74..50b1a15 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -301,7 +301,7 @@ def connect(self):
         self.conn = sqlite3.connect(self.path_results_db)
         self.cursor = self.conn.cursor()
 
-    def create_results_table(self):
+    def create_results_db(self):
         """Generates a new results database."""
 
         if os.path.exists(self.path_results_db):
@@ -309,10 +309,15 @@ def create_results_table(self):
 
         self.connect()
 
-        self.cursor.execute("""CREATE TABLE results (
+        self.cursor.execute("""CREATE TABLE queries (
                                    ms_id TEXT PRIMARY KEY,
                                    exact_mass NUMERIC,
-                                   mc TEXT,
+                                   C INTEGER,
+                                   H INTEGER,
+                                   N INTEGER,
+                                   O INTEGER,
+                                   P INTEGER,
+                                   S INTEGER,
                                    ppm INTEGER,
                                    ha_min INTEGER,
                                    ha_max INTEGER,
@@ -320,8 +325,38 @@ def create_results_table(self):
                                    max_degree INTEGER,
                                    max_n_substructures INTEGER,
                                    hydrogenation_allowance INTEGER,
-                                   isomeric_smiles INTEGER,
-                                   neutral_fragments TEXT)""")
+                                   isomeric_smiles INTEGER)""")
+
+        if self.msn:
+            self.cursor.execute("""CREATE TABLE spectra (
+                                       ms_id TEXT,
+                                       fragment_id NUMERIC,
+                                       neutral_mass NUMERIC,
+                                       PRIMARY KEY (ms_id, fragment_id))""")
+
+        self.cursor.execute("""CREATE TABLE structures (
+                                   ms_id TEXT,
+                                   smiles TEXT,
+                                   frequency NUMERIC,
+                                   exact_mass NUMERIC,
+                                   C INTEGER,
+                                   H INTEGER,
+                                   N INTEGER,
+                                   O INTEGER,
+                                   P INTEGER,
+                                   S INTEGER,
+                                   PRIMARY KEY (ms_id, smiles))""")
+
+        self.cursor.execute("""CREATE TABLE substructures (
+                                           structure_smiles TEXT ,
+                                           substructure_smiles TEXT,
+                                           PRIMARY KEY (structure_smiles, substructure_smiles))""")
+
+        self.cursor.execute("""CREATE TABLE results (
+                                   ms_id TEXT,
+                                   fragment_id NUMERIC,
+                                   structure_smiles TEXT,
+                                   PRIMARY KEY(ms_id, fragment_id, structure_smiles))""")
 
         self.conn.commit()
 

From 030083b5c7bb2bf6c32987543531339721cbf297 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 21 Nov 2020 22:52:32 +0000
Subject: [PATCH 09/35] Update add_ms to add ms information to the queries
 table

---
 metaboblend/build_structures.py | 26 ++++++++++----------------
 1 file changed, 10 insertions(+), 16 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 50b1a15..b11dbfc 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -362,10 +362,10 @@ def create_results_db(self):
 
     def add_ms(self, msn_data, parameters):
         """
-        Add entries to the results table and set up test-specific tables.
+        Add entries to the `queries` and `spectra` tables.
 
         :param msn_data: Dictionary in the form
-            `msn_data[id] = {mf: [C, H, N, O, P, S], exact_mass: float, fragment_masses=[]}`. id represents a unique
+            `msn_data[id] = {mf: [C, H, N, O, P, S], exact_mass: float, fragment_masses: []}`. id represents a unique
             identifier for a given spectral tree or fragmentation spectrum, mf is a list of integers referring to the
             molecular formula of the structure of interest, exact_mass is the mass of this molecular formula to >=4d.p.
             and fragment_masses are neutral fragment masses generated by this structure used to inform candidate
@@ -384,10 +384,10 @@ def add_ms(self, msn_data, parameters):
 
         for ms_id in msn_data.keys():
 
-            self.cursor.execute("""INSERT INTO results (
+            self.cursor.execute("""INSERT INTO queries (
                                        ms_id,
                                        exact_mass,
-                                       mc,
+                                       C, H, N, O, P, S,
                                        ppm,
                                        ha_min,
                                        ha_max,
@@ -395,22 +395,16 @@ def add_ms(self, msn_data, parameters):
                                        max_degree,
                                        max_n_substructures,
                                        hydrogenation_allowance,
-                                       isomeric_smiles,
-                                       neutral_fragments
-                                   ) VALUES ('{}', {}, '{}', {}, '{}')""".format(
+                                       isomeric_smiles
+                                   ) VALUES ('{}', {}, '{}', '{}', '{}', '{}', '{}', '{}', {})""".format(
                                        ms_id,
                                        msn_data[ms_id]["exact_mass"],
-                                       msn_data[ms_id]["mf"],
-                                       ", ".join([str(p) for p in parameters]),
-                                       str(msn_data[ms_id]["fragment_masses"])
+                                       msn_data[ms_id]["mf"][0], msn_data[ms_id]["mf"][1],
+                                       msn_data[ms_id]["mf"][2], msn_data[ms_id]["mf"][3],
+                                       msn_data[ms_id]["mf"][4], msn_data[ms_id]["mf"][5],
+                                       ", ".join([str(p) for p in parameters])
                                    ))
 
-            fragment_statement = "'" + ",\n'".join([str(mass) + "' INTEGER DEFAULT 0" for mass in msn_data[ms_id]["fragment_masses"]])
-            self.cursor.execute("""CREATE TABLE {} (
-                                       smiles TEXT PRIMARY KEY,
-                                       {},
-                                       peak_sum INTEGER DEFAULT 0)""".format(ms_id, fragment_statement))
-
         self.conn.commit()
 
     def add_smis(self, ms_id, smis, fragment_mass):

From b91c1ae6d95e092c2a4595d86e1a3d777b816496 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 21 Nov 2020 22:53:12 +0000
Subject: [PATCH 10/35] Add function to insert entries into the results and
 substructures tables

---
 metaboblend/build_structures.py | 57 +++++++++++++++++++++------------
 1 file changed, 36 insertions(+), 21 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index b11dbfc..d6f14d2 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -407,38 +407,53 @@ def add_ms(self, msn_data, parameters):
 
         self.conn.commit()
 
-    def add_smis(self, ms_id, smis, fragment_mass):
+    def add_results(self, ms_id, smi_dict, fragment_mass=None, fragment_id=None):
         """
         Record which smiles were generated for a given fragment mass.
 
-        :param ms_id: Unique identifer for the annotation of a single metabolite.
+        :param ms_id: Unique identifier for the annotation of a single metabolite.
 
-        :param smis: The smiles generated by the annotation of a single peak for a single metabolite.
+        :param smi_dict: The fragment and substructure smiles generated by the annotation of a single peak for a single
+            metabolite.
 
         :param fragment_mass: The neutral fragment mass that has been annotated.
-        """
-
-        self.cursor.executemany("INSERT OR IGNORE INTO {} (smiles) VALUES (?)".format(ms_id), [[smi] for smi in smis])
-
-        self.cursor.execute("UPDATE {} SET '{}' = 1 WHERE smiles IN ('{}')".format(ms_id,
-                                                                                   str(fragment_mass),
-                                                                                   "', '".join(smis)))
 
-        self.conn.commit()
-
-    def calc_scores(self, ms_id, fragment_masses):
+        :param fragment_id: The unique identifier for the fragment mass that has been annotated.
         """
-        Calculate number of peaks for which a given structure was generated.
 
-        :param ms_id: Unique identifer for the annotation of a single metabolite.
+        if self.msn:
+            self.cursor.execute("""INSERT OR IGNORE INTO spectra (
+                                                       ms_id,
+                                                       fragment_id,
+                                                       neutral_mass
+                                                   ) VALUES ('{}', {}, {})""".format(
+                                                       ms_id,
+                                                       fragment_id,
+                                                       fragment_mass
+                                                   ))
+        else:
+            fragment_id = "NULL"
 
-        :param fragment_masses: The neutral fragment masses that have been annotated.
-        """
+        for structure_smiles in smi_dict.keys():
+            self.cursor.execute("""INSERT INTO results (
+                                       ms_id,
+                                       fragment_id,
+                                       structure_smiles
+                                   ) VALUES ('{}', '{}', '{}')""".format(
+                                       ms_id,
+                                       fragment_id,
+                                       structure_smiles
+                                   ))
+
+            for substructure_smiles in smi_dict[structure_smiles]:
 
-        self.cursor.execute("UPDATE {} SET peak_sum = [{}]".format(
-            ms_id,
-            "] + [".join([str(fragment_mass) for fragment_mass in fragment_masses])
-        ))
+                self.cursor.execute("""INSERT INTO substructures (
+                                                   structure_smiles,
+                                                   substructure_smiles
+                                               ) VALUES ('{}', '{}')""".format(
+                                                   structure_smiles,
+                                                   substructure_smiles
+                                               ))
 
         self.conn.commit()
 

From 41253070711f8cdfa4869936c6bd3798604ddc54 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 21 Nov 2020 22:53:47 +0000
Subject: [PATCH 11/35] Add function to get structure frequencies and/or SMILEs

---
 metaboblend/build_structures.py | 51 +++++++++++++++++++++++++++------
 1 file changed, 43 insertions(+), 8 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index d6f14d2..aa9fe9e 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -457,21 +457,56 @@ def add_results(self, ms_id, smi_dict, fragment_mass=None, fragment_id=None):
 
         self.conn.commit()
 
-    def structure_frequency(self, ms_id):
+    def get_structures(self, ms_id):
         """
-        Generate a dictionary of results with structure frequencies.
+        Gets smiles of generated structures. In the case of the MSn annotation workflow, also gets structure
+        frequencies.
 
-        :param ms_id: Unique identifer for the annotation of a single metabolite.
+        :param ms_id: Unique identifier for the annotation of a single metabolite.
 
-        :return: A dictionary with smiles as keys and the number of peaks for which the smiles were generated as
-        values
+        :return: In the case of simple structure generation, returns a set of smiles strings for output structures.
+            For the MSn annotation workflow, returns a dictionary with smiles as keys and the number of peaks for which
+            the smiles were generated as values.
         """
 
         structure_frequencies = {}
-        for smiles, peak_sum in self.cursor.execute("""SELECT smiles, peak_sum FROM {}""".format(ms_id)):
-            structure_frequencies[smiles] = peak_sum
+        self.cursor.execute("SELECT DISTINCT structure_smiles FROM results WHERE ms_id = '%s'" % ms_id)
+
+        for structure_smiles in self.cursor.fetchall():
+            structure_mol = Chem.MolFromSmiles(structure_smiles[0])
+            structure_mass = calculate_exact_mass(structure_mol)
+            structure_mc = get_elements(structure_mol)
+
+            if self.msn:
+                self.cursor.execute("""SELECT DISTINCT fragment_id FROM results 
+                                           WHERE ms_id = '{}' 
+                                           AND structure_smiles = '{}'""".format(ms_id, structure_smiles[0]))
+
+                structure_frequency = len(self.cursor.fetchall())
+            else:
+                structure_frequency = "NULL"
+
+            self.cursor.execute("""INSERT INTO structures (
+                                       ms_id,
+                                       smiles,
+                                       exact_mass,
+                                       C, H, N, O, P, S,
+                                       frequency
+                                   ) VALUES ('{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')""".format(
+                                       ms_id,
+                                       structure_smiles[0],
+                                       structure_mass,
+                                       structure_mc["C"], structure_mc["H"], structure_mc["N"],
+                                       structure_mc["O"], structure_mc["P"], structure_mc["S"],
+                                       structure_frequency
+                                   ))
 
-        return structure_frequencies
+            structure_frequencies[structure_smiles[0]] = structure_frequency
+
+        if self.msn:
+            return structure_frequencies
+        else:
+            return set(structure_frequencies.keys())
 
     def close(self):
         """Close the connection to the SQLITE3 database"""

From 6017bda2de902de12bac7c8a5d4882916f084ef5 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 21 Nov 2020 22:54:44 +0000
Subject: [PATCH 12/35] Update user-facing functions for compatibility with
 ResultsDb

---
 metaboblend/build_structures.py | 52 +++++++++++++++------------------
 1 file changed, 24 insertions(+), 28 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index aa9fe9e..dda3a1c 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -603,7 +603,7 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
 
     db = SubstructureDb(path_substructure_db, path_connectivity_db)
     results_db = ResultsDb(path_sql_out)
-    results_db.create_results_table()
+    results_db.create_results_db()
 
     # prepare temporary table here - will only be generated once in case of multiple input
     table_name = gen_subs_table(
@@ -621,33 +621,30 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
 
     for i, ms_id in enumerate(msn_data.keys()):
 
-        for prescribed_mass in msn_data[ms_id]["fragment_masses"]:
-            fragment_smis = set()
-            for j in range(0 - hydrogenation_allowance, hydrogenation_allowance + 1):
-                hydrogenated_prescribed_mass = prescribed_mass + (j * 1.007825)  # consider re-arrangements
+        for j, fragment_mass in enumerate(msn_data[ms_id]["fragment_masses"]):
 
-                fragment_smis.update(build(
+            for k in range(0 - hydrogenation_allowance, hydrogenation_allowance + 1):
+                hydrogenated_fragment_mass = fragment_mass + (k * 1.007825)  # consider re-arrangements
+
+                smi_dict = build(
                     mf=msn_data[ms_id]["mf"],
                     exact_mass=msn_data[ms_id]["exact_mass"],
                     max_n_substructures=max_n_substructures,
-                    path_smi_out=None,
                     path_connectivity_db=path_connectivity_db,
                     path_substructure_db=path_substructure_db,
-                    prescribed_mass=hydrogenated_prescribed_mass,
+                    prescribed_mass=hydrogenated_fragment_mass,
                     ppm=ppm,
-                    out_mode=None,
                     table_name=table_name,
                     ncpus=ncpus,
                     clean=False,
                     isomeric_smiles=isomeric_smiles
-                ))
-
-            results_db.add_smis(ms_id, fragment_smis, prescribed_mass)
+                )
 
-        results_db.calc_scores(ms_id, msn_data[ms_id]["fragment_masses"])
+                results_db.add_results(ms_id, smi_dict, fragment_mass, j)
+                fragment_smis = None
 
         if yield_smi_dict:
-            yield {ms_id: results_db.structure_frequency(ms_id)}
+            yield {ms_id: results_db.get_structures(ms_id)}
 
     db.close()
     results_db.close()
@@ -655,18 +652,18 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
 
 def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
                         path_substructure_db: Union[str, bytes, os.PathLike],
+                        path_sql_out: Union[str, bytes, os.PathLike] = "metaboblend_results.sqlite",
                         ha_min: Union[int, None] = 2,
                         ha_max: Union[int, None] = 9,
                         max_degree: int = 6,
                         max_atoms_available: int = 2,
                         max_n_substructures: int = 3,
-                        path_smi_out: Union[str, bytes, os.PathLike, None] = None,
                         ncpus: Union[int, None] = None,
                         path_connectivity_db: Union[str, bytes, os.PathLike, None] = None,
                         minimum_frequency: Union[int, None] = None,
                         yield_smi_set: bool = True,
                         isomeric_smiles: bool = False
-                        ) -> Dict[str, Sequence[list]]:
+                        ) -> Dict[str, Sequence[set]]:
     """
     Generate molecules of a given mass using chemical substructures and connectivity graphs. Can optionally take a
     "prescribed" fragment mass to further filter results. Final structures are returned as a list and/or written in
@@ -682,6 +679,9 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
     :param path_substructure_db: The path to the SQLite 3 substructure database, as generated by
         :py:meth:`metaboblend.databases.SubstructureDb`.
 
+    :param path_sql_out: The path to the SQLite 3 substructure database, as generated by
+        :py:meth:`metaboblend.databases.SubstructureDb`.
+
     :param ha_min: The minimum size (number of heavy atoms) of substructures to be used to build final structures. If
         None, no limit is applied.
 
@@ -708,9 +708,6 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
     :param max_n_substructures: The maximum number of substructures to be used for building molecules. The max number
         of substructures is also limited by the extensivity of the supplied connectivity database.
 
-    :param path_smi_out: The directory to which unique smile strings should be written representing the final
-        structures generated. If None, no files are written.
-
     :param path_connectivity_db: The path to the SQLite 3 connectivity database, as generated by
         :py:meth:`metaboblend.databases.create_isomorphism_database`. If the path is None, the default connectivity
         database bundled with MetaboBlend will be used.
@@ -729,6 +726,8 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
     """
 
     db = SubstructureDb(path_substructure_db, path_connectivity_db)
+    results_db = ResultsDb(path_sql_out, False)
+    results_db.create_results_db()
 
     if path_connectivity_db is None:
         path_connectivity_db = os.path.join(os.path.realpath(os.path.dirname(__file__)), "data", "connectivity.sqlite")
@@ -744,6 +743,8 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
         max_mass=round(max([ms_data[ms_id]["exact_mass"] for ms_id in ms_data.keys()]))
     )
 
+    results_db.add_ms(ms_data, [None, ha_min, ha_max, max_atoms_available, max_degree, max_n_substructures,
+                                None, isomeric_smiles])
     for ms_id in ms_data.keys():
 
         ppm = None
@@ -753,29 +754,24 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
         except KeyError:
             ms_data[ms_id]["prescribed_masses"] = None
 
-        if path_smi_out is not None:
-            id_path_smi_out = os.path.join(path_smi_out, ms_id + ".smi")
-        else:
-            id_path_smi_out = None
-
-        smi_set = build(
+        smi_dict = build(
             mf=ms_data[ms_id]["mf"],
             exact_mass=ms_data[ms_id]["exact_mass"],
             max_n_substructures=max_n_substructures,
-            path_smi_out=id_path_smi_out,
             path_connectivity_db=path_connectivity_db,
             path_substructure_db=path_substructure_db,
             prescribed_mass=ms_data[ms_id]["prescribed_masses"],
             ppm=ppm,
-            out_mode="w",
             table_name=table_name,
             ncpus=ncpus,
             clean=False,
             isomeric_smiles=isomeric_smiles
         )
 
+        results_db.add_results(ms_id, smi_dict, ms_data[ms_id]["prescribed_masses"])
+
         if yield_smi_set:
-            yield {ms_id: smi_set}
+            yield {ms_id: results_db.get_structures(ms_id)}
 
     db.close()
 

From b7f405b29fa5e26e4d37907076b4a58943bf7970 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 21 Nov 2020 22:55:26 +0000
Subject: [PATCH 13/35] Remove text-based output and return substructure smiles
 from build functions in addition to final structures

---
 metaboblend/build_structures.py | 56 ++++++++++++++++-----------------
 1 file changed, 28 insertions(+), 28 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index dda3a1c..275270d 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -776,8 +776,8 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
     db.close()
 
 
-def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_db, path_substructure_db,
-          prescribed_mass, ppm, out_mode, ncpus, table_name, clean, isomeric_smiles):
+def build(mf, exact_mass, max_n_substructures, path_connectivity_db, path_substructure_db,
+          prescribed_mass, ppm, ncpus, table_name, clean, isomeric_smiles):
     """
     Core function for generating molecules of a given mass using substructures and connectivity graphs. Can optionally
     take a "prescribed" fragment mass to further filter results; this can be used to incorporate MSn data. Final
@@ -792,9 +792,6 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
 
     :param exact_mass: The exact mass (float) of the target metabolite.
 
-    :param path_smi_out: The path of the file to which unique smile strings should be written representing the final
-        structures generated. If None, no file is written.
-
     :param max_n_substructures: The maximum number of substructures to be used for building molecules.
 
     :param path_substructure_db: The path to the SQLite 3 substructure database, as generated by
@@ -809,12 +806,6 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
     :param ppm: The maximal tolerated m/z deviation (in parts per million) of the mass of substructures from the
         supplied `fragment_masses`.
 
-    :param out_mode: The mode in which to write to the output file.
-
-        * **"w"** Create a new file, overwriting any existing file.
-
-        * **"w"** Append results to an existing file.
-
     :param ncpus: How many CPUs to utilise; if left as None, :py:meth:`os.cpu_count` is used.
 
     :param table_name: The table specified within the substructure database will be used to generate
@@ -843,7 +834,7 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
         prescribed_subset = [m for m in prescribed_subset[0] if abs(m - prescribed_mass) <= fragment_tolerance]
 
         if len(prescribed_subset) == 0:
-            return set()
+            return {}
 
         loss = exact_mass - prescribed_mass
         exact_mass__1 = round(loss)
@@ -859,13 +850,11 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
     # select groups of masses at low mass resolution
     integer_mass_values = [m for m in db.select_mass_values("1", [], table_name) if m <= exact_mass__1]
     if len(integer_mass_values) == 0:
-        return set()
+        return {}
 
     integer_subsets = list(subset_sum(integer_mass_values, exact_mass__1, max_n_substructures))
 
     configs_iso = db.k_configs()
-    if path_smi_out is not None:
-        smi_out = open(path_smi_out, out_mode)
 
     substructure_subsets = []
     for integer_subset in integer_subsets:
@@ -892,23 +881,23 @@ def build(mf, exact_mass, max_n_substructures, path_smi_out, path_connectivity_d
             substructure_subsets += build_from_subsets(exact_subset, mf=mf, table_name=table_name, db=db)
 
     with multiprocessing.Pool(processes=ncpus) as pool:  # send sets of substructures for building
-        smi_lists = pool.map(
+        smi_dicts = pool.map(
             partial(substructure_combination_build, configs_iso=configs_iso,
                     prescribed_structure=prescribed_mass, isomeric_smiles=isomeric_smiles),
             substructure_subsets
         )
 
-    smis = set([val for sublist in smi_lists for val in sublist])
-
-    if path_smi_out is not None:
-        if len(smis) != 0:
-            smi_out.writelines("\n".join(smis))
-
-        smi_out.close()
+    smi_dict = {}
+    for d in smi_dicts:
+        for k in d.keys():
+            try:
+                smi_dict[k].update(d[k])
+            except KeyError:
+                smi_dict[k] = d[k]
 
     db.close(clean)
 
-    return smis
+    return smi_dict
 
 
 def gen_subs_table(db, ha_min, ha_max, max_degree, max_atoms_available, max_mass, table_name="subset_substructures",
@@ -938,10 +927,10 @@ def gen_subs_table(db, ha_min, ha_max, max_degree, max_atoms_available, max_mass
     :param table_name: Defaults to "subset_substructures", which is cleaned up upon database closure. The name of the
         table to be generated
 
-    :return: The name of the temporary secondary substructure table.
-
     :param minimum_frequency: The minimum frequency of substructures in table_name; e.g. substructures have a frequency
         of 1 if they are unique.
+
+    :return: The name of the temporary secondary substructure table.
     """
 
     db.cursor.execute("DROP TABLE IF EXISTS %s" % table_name)
@@ -1041,10 +1030,14 @@ def substructure_combination_build(substructure_subset, configs_iso, prescribed_
     :param configs_iso: Possible substructure combinations extracted from the connectivity database. A tuple containing
         tuples for each substructure; these tuples specify how many bonds each substructure can make.
 
+    :param prescribed_structure: Prescribed fragment mass for building.
+
+    :param isomeric_smiles: True/False, should output smiles be written with isomeric information?
+
     :return: List of smiles representing molecules generated (and the substructures used to generate them).
     """
 
-    smis = []
+    smis = {}
 
     for substructure_combination in itertools.product(*substructure_subset):
         substructure_combination[0]["fragment"] = True
@@ -1101,8 +1094,15 @@ def substructure_combination_build(substructure_subset, configs_iso, prescribed_
                 continue
 
             try:  # append the canonical smiles of the final structure
-                smis.append(Chem.MolToSmiles(mol_out, isomericSmiles=isomeric_smiles))
+                final_structure = Chem.MolToSmiles(mol_out, isomericSmiles=isomeric_smiles)
             except RuntimeError:
                 continue  # bad bond type violation
 
+            final_substructures = set(subs["smiles"] for subs in substructure_combination)
+
+            try:
+                smis[final_structure].update(final_substructures)
+            except KeyError:
+                smis[final_structure] = final_substructures
+
     return smis

From 3c04cf04bb18eb99e81292e5f1f75d6d1a171f70 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sat, 21 Nov 2020 22:55:43 +0000
Subject: [PATCH 14/35] Update build unit tests for ResultsDb

---
 tests/test_build_structures.py | 123 +++++++++++----------------------
 1 file changed, 41 insertions(+), 82 deletions(-)

diff --git a/tests/test_build_structures.py b/tests/test_build_structures.py
index 87cc2ab..5fa0e44 100644
--- a/tests/test_build_structures.py
+++ b/tests/test_build_structures.py
@@ -67,55 +67,44 @@ def test_build(self):  # core - all other build functions rely on
                 built_smis = build(
                     mf=[record_dict["C"], record_dict["H"], record_dict["N"],
                         record_dict["O"], record_dict["P"], record_dict["S"]],
-                    exact_mass=record_dict["exact_mass"],
-                    path_smi_out=self.to_test_results(record_dict["HMDB_ID"] + ".smi"), max_n_substructures=3,
+                    exact_mass=record_dict["exact_mass"], max_n_substructures=3,
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"), clean=True,
-                    prescribed_mass=None, ppm=None, out_mode="w", ncpus=None, table_name="substructures",
+                    prescribed_mass=None, ppm=None, ncpus=None, table_name="substructures",
                     isomeric_smiles=True
                 )
 
                 j = 0
-                unique_smis = set()
-                with open(self.to_test_results(record_dict["HMDB_ID"] + ".smi"), "r") as smi_out:
-                    for line in smi_out:
-                        j += 1
-                        unique_smis.add(line.split()[0])
 
-                self.assertEqual(unique_smis, built_smis)
+                for smi in built_smis:
+                    j += 1
+
                 self.assertEqual(j, std_lens[i])
 
                 if smis[i] is not None:
-                    self.assertEqual(unique_smis, smis[i])
+                    self.assertEqual(set(built_smis.keys()), smis[i])
                 else:
-                    self.assertTrue(len(unique_smis) == 51 or len(unique_smis) == 1892)
+                    self.assertTrue(len(built_smis.keys()) == 51 or len(built_smis.keys()) == 1892)
 
                 # test prescribed substructure building
                 built_smis = build(
                     mf=[record_dict["C"], record_dict["H"], record_dict["N"],
                         record_dict["O"], record_dict["P"], record_dict["S"]],
-                    exact_mass=record_dict["exact_mass"],
-                    path_smi_out=self.to_test_results(record_dict["HMDB_ID"] + ".smi"), max_n_substructures=3,
+                    exact_mass=record_dict["exact_mass"], max_n_substructures=3,
                     prescribed_mass=fragments[i], ppm=15, clean=True,
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    out_mode="w", ncpus=None, table_name="substructures",
-                    isomeric_smiles=True
+                    ncpus=None, table_name="substructures", isomeric_smiles=True
                 )
 
                 j = 0
-                unique_smis = set()
-                with open(self.to_test_results(record_dict["HMDB_ID"] + ".smi"), "r") as smi_out:
-                    for line in smi_out:
-                        j += 1
-                        unique_smis.add(line.split()[0])
-
-                self.assertEqual(unique_smis, built_smis)
+                for smi in built_smis:
+                    j += 1
 
                 if i == 2:
-                    self.assertEqual(unique_smis, {'N[C@@H](Cc1ccc(O)cc1)C(=O)O', 'N[C@@H](Cc1cccc(O)c1)C(=O)O'})
+                    self.assertEqual(set(built_smis.keys()), {'N[C@@H](Cc1ccc(O)cc1)C(=O)O', 'N[C@@H](Cc1cccc(O)c1)C(=O)O'})
 
-                self.assertEqual(len(unique_smis), exp_lens[i])
+                self.assertEqual(len(built_smis.keys()), exp_lens[i])
 
         db.close()
 
@@ -127,17 +116,17 @@ def test_substructure_combination_build(self):
                        ((5, 5, 0, 2, 0, 0), (3, 6, 1, 0, 0, 0)),
                        ((2, 4, 0, 2, 0, 0), (4, 8, 0, 4, 0, 0))]
         configs_iso = db.k_configs()
-        lens = [0, 1, 60]
+        lens = [0, 1, 41]
 
         for i, ec_product in enumerate(ec_products):
             substructure_subset = db.select_substructures(ec_product, "substructures")
             smis = substructure_combination_build(substructure_subset, configs_iso,
                                                   prescribed_structure=False, isomeric_smiles=True)
 
-            self.assertEqual(len(smis), lens[i])
+            self.assertEqual(len(smis.keys()), lens[i])
 
             if i == 1:
-                self.assertEqual(smis, ['NCCc1ccc(O)c(O)c1'])
+                self.assertEqual(list(smis.keys()), ['NCCc1ccc(O)c(O)c1'])
 
         db.close()
 
@@ -185,14 +174,12 @@ def test_generate_structures(self):  # tests vs build
                                                     "exact_mass": record_dict["exact_mass"]}}
 
                 # test standard building
-                returned_smis = list(generate_structures(
-                    ms_data, max_degree=6, max_atoms_available=2, max_n_substructures=3,
-                    path_smi_out=self.to_test_results(),
-                    path_connectivity_db=self.to_test_data("connectivity.sqlite"),
-                    path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    minimum_frequency=None, yield_smi_set=True,
-                    isomeric_smiles=True
-                ))
+                returned_smis = list(
+                    generate_structures(ms_data, path_substructure_db=self.to_test_data("substructures.sqlite"),
+                                        path_sql_out=self.to_test_results("results.sqlite"),
+                                        max_degree=6, max_atoms_available=2, max_n_substructures=3,
+                                        path_connectivity_db=self.to_test_data("connectivity.sqlite"),
+                                        minimum_frequency=None, yield_smi_set=True, isomeric_smiles=True))
 
                 returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
 
@@ -200,20 +187,12 @@ def test_generate_structures(self):  # tests vs build
                     mf=[record_dict["C"], record_dict["H"], record_dict["N"],
                         record_dict["O"], record_dict["P"], record_dict["S"]],
                     exact_mass=record_dict["exact_mass"],
-                    path_smi_out=self.to_test_results(record_dict["HMDB_ID"] + "_build.smi"),
                     max_n_substructures=3, path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"), clean=True,
-                    prescribed_mass=None, ppm=None, out_mode="w", ncpus=None, table_name="substructures",
-                    isomeric_smiles=True
+                    prescribed_mass=None, ppm=None, ncpus=None, table_name="substructures", isomeric_smiles=True
                 )
 
-                unique_smis = set()
-                with open(self.to_test_results(record_dict["HMDB_ID"] + ".smi"), "r") as smi_out:
-                    for line in smi_out:
-                        unique_smis.add(line.split()[0])
-
-                self.assertEqual(unique_smis, returned_smis)
-                self.assertEqual(unique_smis, build_smis)
+                self.assertEqual(set(build_smis.keys()), returned_smis)
 
                 ms_data = {record_dict["HMDB_ID"]: {"mf": [record_dict["C"], record_dict["H"], record_dict["N"],
                                                            record_dict["O"], record_dict["P"], record_dict["S"]],
@@ -221,36 +200,26 @@ def test_generate_structures(self):  # tests vs build
                                                     "prescribed_masses": fragments[i]}}
 
                 # test prescribed building
-                returned_smis = list(generate_structures(
-                    ms_data, max_degree=6, max_atoms_available=2, max_n_substructures=3,
-                    path_smi_out=self.to_test_results(),
-                    path_connectivity_db=self.to_test_data("connectivity.sqlite"),
-                    path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    minimum_frequency=None, yield_smi_set=True,
-                    isomeric_smiles=True
-                ))
+                returned_smis = list(
+                    generate_structures(ms_data, path_substructure_db=self.to_test_data("substructures.sqlite"),
+                                        path_sql_out=self.to_test_results("results.sqlite"),
+                                        max_degree=6, max_atoms_available=2, max_n_substructures=3,
+                                        path_connectivity_db=self.to_test_data("connectivity.sqlite"),
+                                        minimum_frequency=None, yield_smi_set=True, isomeric_smiles=True))
 
                 returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
 
                 build_smis = build(
                     mf=[record_dict["C"], record_dict["H"], record_dict["N"],
                         record_dict["O"], record_dict["P"], record_dict["S"]],
-                    exact_mass=record_dict["exact_mass"],
-                    path_smi_out=self.to_test_results(record_dict["HMDB_ID"] + "_build.smi"), max_n_substructures=3,
+                    exact_mass=record_dict["exact_mass"], max_n_substructures=3,
                     prescribed_mass=fragments[i], ppm=0,
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    out_mode="w", ncpus=None, table_name="substructures", clean=True,
-                    isomeric_smiles=True
+                    ncpus=None, table_name="substructures", clean=True, isomeric_smiles=True
                 )
 
-                unique_smis = set()
-                with open(self.to_test_results(record_dict["HMDB_ID"] + ".smi"), "r") as smi_out:
-                    for line in smi_out:
-                        unique_smis.add(line.split()[0])
-
-                self.assertEqual(unique_smis, returned_smis)
-                self.assertEqual(unique_smis, build_smis)
+                self.assertEqual(set(build_smis.keys()), returned_smis)
 
             ms_data = {}
             for i, record_dict in enumerate(record_dicts.values()):
@@ -261,34 +230,24 @@ def test_generate_structures(self):  # tests vs build
                                                    "prescribed_masses": None}
 
             # test building with multiple inputs
-            returned_smi_list = list(generate_structures(
-                ms_data, max_degree=6, max_atoms_available=2, max_n_substructures=3,
-                path_smi_out=self.to_test_results(),
-                path_connectivity_db=self.to_test_data("connectivity.sqlite"),
-                path_substructure_db=self.to_test_data("substructures.sqlite"),
-                minimum_frequency=None, yield_smi_set=True,
-                isomeric_smiles=True
-            ))
+            returned_smi_list = list(
+                generate_structures(ms_data, path_substructure_db=self.to_test_data("substructures.sqlite"),
+                                    path_sql_out=self.to_test_results("results.sqlite"),
+                                    max_degree=6, max_atoms_available=2, max_n_substructures=3,
+                                    path_connectivity_db=self.to_test_data("connectivity.sqlite"),
+                                    minimum_frequency=None, yield_smi_set=True, isomeric_smiles=True))
 
             for i, record_dict in enumerate(record_dicts.values()):
                 build_smis = build(
                     mf=[record_dict["C"], record_dict["H"], record_dict["N"],
                         record_dict["O"], record_dict["P"], record_dict["S"]],
                     exact_mass=record_dict["exact_mass"],
-                    path_smi_out=self.to_test_results(record_dict["HMDB_ID"] + "_build.smi"),
                     max_n_substructures=3, path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"), clean=True,
-                    prescribed_mass=None, ppm=None, out_mode="w", ncpus=None, table_name="substructures",
-                    isomeric_smiles=True
+                    prescribed_mass=None, ppm=None, ncpus=None, table_name="substructures", isomeric_smiles=True
                 )
 
-                unique_smis = set()
-                with open(self.to_test_results(record_dict["HMDB_ID"] + ".smi"), "r") as smi_out:
-                    for line in smi_out:
-                        unique_smis.add(line.split()[0])
-
-                self.assertEqual(unique_smis, returned_smi_list[i][record_dict["HMDB_ID"]])
-                self.assertEqual(unique_smis, build_smis)
+                self.assertEqual(set(build_smis.keys()), returned_smi_list[i][record_dict["HMDB_ID"]])
 
         db.close()
 

From 8a9dd0e2a7b7a7e87cd94c3641258f514d919ba6 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sun, 22 Nov 2020 01:19:32 +0000
Subject: [PATCH 15/35] Add CSV output for build functions

---
 metaboblend/build_structures.py | 57 +++++++++++++++++++++++++++------
 1 file changed, 47 insertions(+), 10 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 275270d..8fc9239 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -27,6 +27,7 @@
 import networkx as nx
 import numpy
 import sqlite3
+import csv
 from operator import itemgetter
 from typing import Sequence, Dict, Union
 
@@ -283,13 +284,14 @@ class ResultsDb:
     Methods for interacting with the SQLITE3 results database, as created by
     :py:meth:`metaboblend.build_structures.annotate_msn`.
 
-    :param path_results_db: Path to the results database.
+    :param path_results: Directory to which results will be written.
     """
 
-    def __init__(self, path_results_db, msn=True):
+    def __init__(self, path_results, msn=True):
         """Constructor method."""
 
-        self.path_results_db = path_results_db
+        self.path_results = path_results
+        self.path_results_db = os.path.join(self.path_results, "metaboblend_results.sqlite")
         self.msn = msn
 
         self.conn = None
@@ -508,15 +510,43 @@ def get_structures(self, ms_id):
         else:
             return set(structure_frequencies.keys())
 
+    def generate_csv_output(self):
+        """
+        Generate CSV file output for i) queries and tool parameters and ii) structures generated.
+        """
+
+        with open(os.path.join(self.path_results, "metaboblend_queries.csv"), "w", newline="") as results_file, \
+             open(os.path.join(self.path_results, "metaboblend_structures.csv"), "w", newline="") as ms_file:
+
+            results_writer = csv.writer(results_file, delimiter=",")
+            ms_writer = csv.writer(ms_file, delimiter=",")
+
+            results_writer.writerow(["ms_id", "exact_mass", "C", "H", "N", "O", "P", "S", "ppm", "ha_min", "ha_max",
+                                     "max_atoms_available", "max_degree", "max_n_substructures",
+                                     "hydrogenation_allowance", "isomeric_smiles"])
+
+            self.cursor.execute("SELECT * FROM queries")
+
+            for query in self.cursor.fetchall():
+                results_writer.writerow(query)
+
+            ms_writer.writerow(["ms_id", "smiles", "frequency", "exact_mass", "C", "H", "N", "O", "P", "S"])
+
+            self.cursor.execute("SELECT * FROM structures")
+
+            for structure in self.cursor.fetchall():
+                ms_writer.writerow(structure)
+
     def close(self):
-        """Close the connection to the SQLITE3 database"""
+        """Close the connection to the SQLITE3 database."""
 
         self.conn.close()
 
 
 def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
-                 path_substructure_db: Union[str, bytes, os.PathLike],
-                 path_sql_out: Union[str, bytes, os.PathLike] = "metaboblend_results.sqlite",
+                 path_substructure_db: Union[str, bytes, os.PathLike] = os.path.realpath(os.getcwd()),
+                 path_out: Union[str, bytes, os.PathLike] = "",
+                 write_csv_output: bool = False,
                  ppm: int = 5,
                  ha_min: Union[int, None] = None,
                  ha_max: Union[int, None] = None,
@@ -545,7 +575,7 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
     :param path_substructure_db: The path to the SQLite 3 substructure database, as generated by
         :py:meth:`metaboblend.databases.SubstructureDb`.
 
-    :param path_sql_out: The path at which an SQLite database will be generated to store results.
+    :param path_out: Folder to which the SQLite 3 results database and CSV outputs should be written.
 
     :param ppm: The maximal tolerated m/z deviation (in parts per million) of the mass of substructures from the
         supplied `fragment_masses`.
@@ -602,7 +632,7 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
         path_connectivity_db = os.path.join(os.path.realpath(os.path.dirname(__file__)), "data", "connectivity.sqlite")
 
     db = SubstructureDb(path_substructure_db, path_connectivity_db)
-    results_db = ResultsDb(path_sql_out)
+    results_db = ResultsDb(path_out)
     results_db.create_results_db()
 
     # prepare temporary table here - will only be generated once in case of multiple input
@@ -646,13 +676,17 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
         if yield_smi_dict:
             yield {ms_id: results_db.get_structures(ms_id)}
 
+    if write_csv_output:
+        results_db.generate_csv_output()
+
     db.close()
     results_db.close()
 
 
 def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
                         path_substructure_db: Union[str, bytes, os.PathLike],
-                        path_sql_out: Union[str, bytes, os.PathLike] = "metaboblend_results.sqlite",
+                        path_out: Union[str, bytes, os.PathLike] = os.path.realpath(os.getcwd()),
+                        write_csv_output: bool = False,
                         ha_min: Union[int, None] = 2,
                         ha_max: Union[int, None] = 9,
                         max_degree: int = 6,
@@ -726,7 +760,7 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
     """
 
     db = SubstructureDb(path_substructure_db, path_connectivity_db)
-    results_db = ResultsDb(path_sql_out, False)
+    results_db = ResultsDb(path_out, False)
     results_db.create_results_db()
 
     if path_connectivity_db is None:
@@ -773,6 +807,9 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
         if yield_smi_set:
             yield {ms_id: results_db.get_structures(ms_id)}
 
+    if write_csv_output:
+        results_db.generate_csv_output()
+
     db.close()
 
 

From c75ac3864cf84d6a1b75d5a9177c21d8fa286f25 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sun, 22 Nov 2020 01:21:20 +0000
Subject: [PATCH 16/35] Check if ResultsDb output matches reference files

---
 tests/test_build_structures.py             | 50 +++++++++++++++++++---
 tests/test_data/metaboblend_queries.csv    |  5 +++
 tests/test_data/metaboblend_structures.csv | 47 ++++++++++++++++++++
 tests/test_suite_build_structures.py       |  2 +-
 4 files changed, 97 insertions(+), 7 deletions(-)
 create mode 100644 tests/test_data/metaboblend_queries.csv
 create mode 100644 tests/test_data/metaboblend_structures.csv

diff --git a/tests/test_build_structures.py b/tests/test_build_structures.py
index 5fa0e44..c1c9c21 100644
--- a/tests/test_build_structures.py
+++ b/tests/test_build_structures.py
@@ -22,6 +22,7 @@
 
 import unittest
 import shutil
+import filecmp
 import tempfile
 from metaboblend.build_structures import *
 from metaboblend.databases import *
@@ -176,7 +177,7 @@ def test_generate_structures(self):  # tests vs build
                 # test standard building
                 returned_smis = list(
                     generate_structures(ms_data, path_substructure_db=self.to_test_data("substructures.sqlite"),
-                                        path_sql_out=self.to_test_results("results.sqlite"),
+                                        write_csv_output=True, path_out=self.to_test_results(),
                                         max_degree=6, max_atoms_available=2, max_n_substructures=3,
                                         path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                                         minimum_frequency=None, yield_smi_set=True, isomeric_smiles=True))
@@ -202,7 +203,7 @@ def test_generate_structures(self):  # tests vs build
                 # test prescribed building
                 returned_smis = list(
                     generate_structures(ms_data, path_substructure_db=self.to_test_data("substructures.sqlite"),
-                                        path_sql_out=self.to_test_results("results.sqlite"),
+                                        write_csv_output=True, path_out=self.to_test_results(),
                                         max_degree=6, max_atoms_available=2, max_n_substructures=3,
                                         path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                                         minimum_frequency=None, yield_smi_set=True, isomeric_smiles=True))
@@ -232,7 +233,7 @@ def test_generate_structures(self):  # tests vs build
             # test building with multiple inputs
             returned_smi_list = list(
                 generate_structures(ms_data, path_substructure_db=self.to_test_data("substructures.sqlite"),
-                                    path_sql_out=self.to_test_results("results.sqlite"),
+                                    write_csv_output=True, path_out=self.to_test_results(),
                                     max_degree=6, max_atoms_available=2, max_n_substructures=3,
                                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                                     minimum_frequency=None, yield_smi_set=True, isomeric_smiles=True))
@@ -278,7 +279,7 @@ def test_annotate_msn(self):  # tests vs build_msn
                 # test standard building
                 returned_smis = list(annotate_msn(
                     ms_data, max_degree=6, max_atoms_available=2, max_n_substructures=3,
-                    path_sql_out=self.to_test_results("results.sqlite"),
+                    write_csv_output=True, path_out=self.to_test_results(),
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
                     minimum_frequency=None, yield_smi_dict=True, isomeric_smiles=True
@@ -307,7 +308,7 @@ def test_annotate_msn(self):  # tests vs build_msn
             # test building with multiple inputs
             returned_smi_list = list(annotate_msn(
                 ms_data, max_degree=6, max_atoms_available=2, max_n_substructures=3,
-                path_sql_out=self.to_test_results("annotate_multi", "results.sqlite"),
+                path_out=self.to_test_results("annotate_multi"), write_csv_output=True,
                 path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                 path_substructure_db=self.to_test_data("substructures.sqlite"),
                 minimum_frequency=None, yield_smi_dict=True,
@@ -316,9 +317,46 @@ def test_annotate_msn(self):  # tests vs build_msn
 
             for i, record_dict in enumerate(record_dicts.values()):
                 self.assertEqual(len(set(returned_smi_list[i][record_dict["HMDB_ID"]].keys())), overall_lens[i])
-
+        
         db.close()
 
+    def test_results_db(self):
+        fragments = [56.05, 60.0211, 68.0262, 56.0262]
+
+        with open(self.to_test_data("test_hmdbs.dictionary"), "rb") as test_hmdbs:
+            record_dicts = pickle.load(test_hmdbs)
+
+        ms_data = {}
+        for i, record_dict in enumerate(record_dicts.values()):
+            record_dict["mol"] = Chem.MolFromSmiles(record_dict["smiles"])
+            ms_data[record_dict["HMDB_ID"]] = {"mf": [record_dict["C"], record_dict["H"], record_dict["N"],
+                                                      record_dict["O"], record_dict["P"], record_dict["S"]],
+                                               "exact_mass": record_dict["exact_mass"],
+                                               "fragment_masses": fragments}
+
+        os.mkdir(self.to_test_results("test_results_db"))
+
+        list(annotate_msn(
+            ms_data, max_degree=6, max_atoms_available=2, max_n_substructures=3,
+            path_out=self.to_test_results("test_results_db"), write_csv_output=True,
+            path_connectivity_db=self.to_test_data("connectivity.sqlite"),
+            path_substructure_db=self.to_test_data("substructures.sqlite"),
+            minimum_frequency=None, yield_smi_dict=True,
+            isomeric_smiles=True
+        ))
+
+        # is the sqlite database the size we expect?
+        self.assertEqual(os.path.getsize(self.to_test_results("test_results_db", "metaboblend_results.sqlite")), 61440)
+
+        # do the files generated by ResultsDb match what we expect? if not, find differences
+        self.assertTrue(filecmp.cmp(self.to_test_results("test_results_db", "metaboblend_queries.csv"),
+                                    self.to_test_data("metaboblend_queries.csv"),
+                                    shallow=False))
+
+        self.assertTrue(filecmp.cmp(self.to_test_results("test_results_db", "metaboblend_structures.csv"),
+                                    self.to_test_data("metaboblend_structures.csv"),
+                                    shallow=False))
+
     def test_gen_subs_table(self):
         db = SubstructureDb(self.to_test_data("substructures.sqlite"), "")
         table_name = gen_subs_table(db, 5, 6, 4, 2, 500)
diff --git a/tests/test_data/metaboblend_queries.csv b/tests/test_data/metaboblend_queries.csv
new file mode 100644
index 0000000..5f6dd89
--- /dev/null
+++ b/tests/test_data/metaboblend_queries.csv
@@ -0,0 +1,5 @@
+ms_id,exact_mass,C,H,N,O,P,S,ppm,ha_min,ha_max,max_atoms_available,max_degree,max_n_substructures,hydrogenation_allowance,isomeric_smiles
+HMDB0000073,153.078979,8,11,1,2,0,0,5,,,2,6,3,2,1
+HMDB0000122,180.06339,6,12,0,6,0,0,5,,,2,6,3,2,1
+HMDB0000158,181.073894,9,11,1,3,0,0,5,,,2,6,3,2,1
+HMDB0000186,342.116215,12,22,0,11,0,0,5,,,2,6,3,2,1
diff --git a/tests/test_data/metaboblend_structures.csv b/tests/test_data/metaboblend_structures.csv
new file mode 100644
index 0000000..2958015
--- /dev/null
+++ b/tests/test_data/metaboblend_structures.csv
@@ -0,0 +1,47 @@
+ms_id,smiles,frequency,exact_mass,C,H,N,O,P,S
+HMDB0000073,NCCc1ccc(O)c(O)c1,3,153.07897899999998,8,11,1,2,0,0
+HMDB0000073,NCCc1cc(O)cc(O)c1,1,153.07897899999998,8,11,1,2,0,0
+HMDB0000073,NCCc1cc(O)ccc1O,1,153.07897899999998,8,11,1,2,0,0
+HMDB0000122,OC1C(O)[C@H](O)[C@@H](O)[C@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC1[C@H](O)C(O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC1[C@H](O)[C@@H](O)[C@@H](O)[C@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC1[C@H](O)[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC1[C@H](O)[C@H](O)[C@@H](O)[C@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC1[C@H](O)[C@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)C(O)[C@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)O[C@H](CO)C1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@@H](O)C(O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@@H](O)[C@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@H](O)C(O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@H](O)O[C@@H]1CO,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@@H](CO)O1,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@@H](O)C1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OO[C@H](CO)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1OO[C@H](CO)[C@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@@H](O)C(O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@@H](O)C(O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@@H](O)[C@@H](O)[C@@H](CO)O1,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@@H](O)[C@@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@@H](O)[C@@H](O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](CO)O1,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)C1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@@H](CO)OC1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@@H](O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)C(O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)O[C@@H]1CO,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](CO)O1,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](O)C1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
+HMDB0000158,N[C@@H](Cc1ccc(O)cc1)C(=O)O,1,181.07389399999997,9,11,1,3,0,0
+HMDB0000158,N[C@@H](Cc1cccc(O)c1)C(=O)O,1,181.07389399999997,9,11,1,3,0,0
diff --git a/tests/test_suite_build_structures.py b/tests/test_suite_build_structures.py
index 48176ba..0cc6ee1 100644
--- a/tests/test_suite_build_structures.py
+++ b/tests/test_suite_build_structures.py
@@ -36,4 +36,4 @@
     suite.addTest(unittest.findTestCases(test_build_structures))
 
     report = os.path.join(os.path.abspath(os.path.join(__file__, os.pardir)), 'results', 'results_test_suite_build_structures')
-    runTestSuite(suite, report, title='Process Test Suite Report',verbosity=2)
+    runTestSuite(suite, report, title='Process Test Suite Report', verbosity=2)

From 94e31cd150e15e9b48f82f421a0a10968b0f672f Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Sun, 22 Nov 2020 01:43:01 +0000
Subject: [PATCH 17/35] Check ResultsDb CSV files line by line vs reference

---
 tests/test_build_structures.py | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/tests/test_build_structures.py b/tests/test_build_structures.py
index c1c9c21..167c118 100644
--- a/tests/test_build_structures.py
+++ b/tests/test_build_structures.py
@@ -22,7 +22,6 @@
 
 import unittest
 import shutil
-import filecmp
 import tempfile
 from metaboblend.build_structures import *
 from metaboblend.databases import *
@@ -348,14 +347,18 @@ def test_results_db(self):
         # is the sqlite database the size we expect?
         self.assertEqual(os.path.getsize(self.to_test_results("test_results_db", "metaboblend_results.sqlite")), 61440)
 
-        # do the files generated by ResultsDb match what we expect? if not, find differences
-        self.assertTrue(filecmp.cmp(self.to_test_results("test_results_db", "metaboblend_queries.csv"),
-                                    self.to_test_data("metaboblend_queries.csv"),
-                                    shallow=False))
+        # are the csv files the same as the reference?
+        with open(self.to_test_results("test_results_db", "metaboblend_queries.csv"), "r") as results_file, \
+             open(self.to_test_data("metaboblend_queries.csv"), "r") as test_file:
 
-        self.assertTrue(filecmp.cmp(self.to_test_results("test_results_db", "metaboblend_structures.csv"),
-                                    self.to_test_data("metaboblend_structures.csv"),
-                                    shallow=False))
+            for results_line, test_line in zip(results_file, test_file):
+                self.assertEqual(results_line, test_line)
+
+        with open(self.to_test_results("test_results_db", "metaboblend_structures.csv"), "r") as results_file, \
+             open(self.to_test_data("metaboblend_structures.csv"), "r") as test_file:
+
+            for results_line, test_line in zip(results_file, test_file):
+                self.assertEqual(results_line, test_line)
 
     def test_gen_subs_table(self):
         db = SubstructureDb(self.to_test_data("substructures.sqlite"), "")

From 38c8ee0ab15bed436522b723b5ff543b21af3927 Mon Sep 17 00:00:00 2001
From: Jack Gisby <jackgisby@gmail.com>
Date: Sun, 22 Nov 2020 14:28:00 +0000
Subject: [PATCH 18/35] Implement simple bond dissociation energy calculations

---
 metaboblend/build_structures.py | 82 ++++++++++++++++++++++++---------
 tests/test_build_structures.py  |  5 +-
 2 files changed, 64 insertions(+), 23 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 8fc9239..ea450ec 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -214,7 +214,7 @@ def reindex_atoms(records):
     return mol_comb, atoms_available, atoms_to_remove, bond_types, bond_mismatch
 
 
-def add_bonds(mols, edges, atoms_available, bond_types):
+def add_bonds(mols, edges, atoms_available, bond_types, bond_enthalpies):
     """
     Takes a set of substructures and attempts to combine them together to generate a final structure. One of the last
     steps in the :py:meth:`metaboblend.build_structures.build` workflow.
@@ -244,28 +244,32 @@ def add_bonds(mols, edges, atoms_available, bond_types):
                         1.5: Chem.rdchem.BondType.AROMATIC,
                         2: Chem.rdchem.BondType.DOUBLE}
 
+    bond_types_copy = copy.deepcopy(bond_types)  # deep copy as we modify items within the dict
+
     g = nx.Graph()
     g.add_edges_from(edges)
 
     g = nx.relabel_nodes(g, dict(zip(sorted(g.nodes()), atoms_available)))
 
+    total_bde = 0
+
     mol_edit = Chem.EditableMol(mols)
     for edge in g.edges():
 
-        if edge[0] in bond_types:
-            bt_start = copy.copy(bond_types[edge[0]])
+        if edge[0] in bond_types_copy:
+            bt_start = bond_types_copy[edge[0]]
         else:
-            return None  # nested dummy
+            return None, None  # nested dummy
 
-        if edge[1] in bond_types:
-            bt_end = copy.copy(bond_types[edge[1]])
+        if edge[1] in bond_types_copy:
+            bt_end = bond_types_copy[edge[1]]
         else:
-            return None  # nested dummy
+            return None, None  # nested dummy
 
         bond_matches = list(set(bt_start).intersection(bt_end))
 
         if len(bond_matches) == 0:
-            return None
+            return None, None
 
         else:
             bt_start.remove(bond_matches[0])
@@ -274,9 +278,14 @@ def add_bonds(mols, edges, atoms_available, bond_types):
         try:
             mol_edit.AddBond(edge[0], edge[1], rdkit_bond_types[bond_matches[0]])
         except KeyError:
-            return None  # unknown bond type
+            return None, None  # unknown bond type
+
+        try:
+            total_bde += bond_enthalpies[bond_matches[0]][mols.GetAtomWithIdx(edge[0]).GetSymbol()][mols.GetAtomWithIdx(edge[1]).GetSymbol()]
+        except SyntaxError:
+            total_bde = None
 
-    return mol_edit
+    return mol_edit, total_bde
 
 
 class ResultsDb:
@@ -358,6 +367,7 @@ def create_results_db(self):
                                    ms_id TEXT,
                                    fragment_id NUMERIC,
                                    structure_smiles TEXT,
+                                   bde NUMERIC,
                                    PRIMARY KEY(ms_id, fragment_id, structure_smiles))""")
 
         self.conn.commit()
@@ -440,14 +450,16 @@ def add_results(self, ms_id, smi_dict, fragment_mass=None, fragment_id=None):
             self.cursor.execute("""INSERT INTO results (
                                        ms_id,
                                        fragment_id,
-                                       structure_smiles
-                                   ) VALUES ('{}', '{}', '{}')""".format(
+                                       structure_smiles,
+                                       bde
+                                   ) VALUES ('{}', '{}', '{}', '{}')""".format(
                                        ms_id,
                                        fragment_id,
-                                       structure_smiles
+                                       structure_smiles,
+                                       smi_dict[structure_smiles][0]
                                    ))
 
-            for substructure_smiles in smi_dict[structure_smiles]:
+            for substructure_smiles in smi_dict[structure_smiles][1]:
 
                 self.cursor.execute("""INSERT INTO substructures (
                                                    structure_smiles,
@@ -920,7 +932,8 @@ def build(mf, exact_mass, max_n_substructures, path_connectivity_db, path_substr
     with multiprocessing.Pool(processes=ncpus) as pool:  # send sets of substructures for building
         smi_dicts = pool.map(
             partial(substructure_combination_build, configs_iso=configs_iso,
-                    prescribed_structure=prescribed_mass, isomeric_smiles=isomeric_smiles),
+                    prescribed_structure=prescribed_mass, isomeric_smiles=isomeric_smiles,
+                    bond_enthalpies=get_bond_enthalpies()),
             substructure_subsets
         )
 
@@ -928,7 +941,11 @@ def build(mf, exact_mass, max_n_substructures, path_connectivity_db, path_substr
     for d in smi_dicts:
         for k in d.keys():
             try:
-                smi_dict[k].update(d[k])
+                smi_dict[k][1].update(d[k][1])
+
+                if d[k][0] < smi_dict[k][0]:
+                    smi_dict[k][0] = d[k][0]
+
             except KeyError:
                 smi_dict[k] = d[k]
 
@@ -1057,7 +1074,26 @@ def build_from_subsets(exact_subset, mf, table_name, db):
     return substructure_subsets
 
 
-def substructure_combination_build(substructure_subset, configs_iso, prescribed_structure, isomeric_smiles):
+def get_bond_enthalpies():
+
+    return {1.0:   {'C': {'C': 348, 'N': 305, 'O': 360, 'P': 264, 'S': 272},
+                         'N': {'C': 305, 'N': 163, 'O': 222, 'P': None, 'S': None},
+                         'O': {'C': 360, 'N': 222, 'O': 146, 'P': 335, 'S': None},
+                         'P': {'C': 264, 'N': None, 'O': 335, 'P': 201, 'S': None},
+                         'S': {'C': 272, 'N': None, 'O': None, 'P': None, 'S': 226}},
+            1.5: {'C': {'C': 837, 'N': 890, 'O': None, 'P': None, 'S': None},
+                         'N': {'C': 890, 'N': 944, 'O': None, 'P': None, 'S': None},
+                         'O': {'C': None, 'N': None, 'O': None, 'P': None, 'S': None},
+                         'P': {'C': None, 'N': None, 'O': None, 'P': None, 'S': None},
+                         'S': {'C': None, 'N': None, 'O': None, 'P': None, 'S': None}},
+            2.0:   {'C': {'C': 612, 'N': 613, 'O': 743, 'P': None, 'S': 573},
+                         'N': {'C': 613, 'N': 409, 'O': 607, 'P': None, 'S': None},
+                         'O': {'C': 743, 'N': 607, 'O': 496, 'P': 544, 'S': 522},
+                         'P': {'C': None, 'N': None, 'O': 544, 'P': None, 'S': 335},
+                         'S': {'C': 573, 'N': None, 'O': 522, 'P': 335, 'S': 425}}}
+
+
+def substructure_combination_build(substructure_subset, configs_iso, prescribed_structure, isomeric_smiles, bond_enthalpies):
     """
     Final stage for building molecules; takes a combination of substructures (substructure_combination) and builds them
     according to graphs in the substructure database. May be run in parallel.
@@ -1115,9 +1151,9 @@ def substructure_combination_build(substructure_subset, configs_iso, prescribed_
                 if non_fragment_edges:
                     continue
 
-            mol_e = add_bonds(mol_comb, edges, atoms_available, bond_types)  # add bonds between substructures
+            mol_e, total_bde = add_bonds(mol_comb, edges, atoms_available, bond_types, bond_enthalpies)  # add bonds between substructures
 
-            if mol_e is None:
+            if mol_e is None or total_bde is None:
                 continue
 
             atoms_to_remove.sort(reverse=True)
@@ -1138,8 +1174,12 @@ def substructure_combination_build(substructure_subset, configs_iso, prescribed_
             final_substructures = set(subs["smiles"] for subs in substructure_combination)
 
             try:
-                smis[final_structure].update(final_substructures)
+                smis[final_structure][1].update(final_substructures)
+
+                if total_bde < smis[final_structure][0]:
+                    smis[final_structure][0] = total_bde
+
             except KeyError:
-                smis[final_structure] = final_substructures
+                smis[final_structure] = [total_bde, final_substructures]
 
     return smis
diff --git a/tests/test_build_structures.py b/tests/test_build_structures.py
index 167c118..dcbab3f 100644
--- a/tests/test_build_structures.py
+++ b/tests/test_build_structures.py
@@ -121,7 +121,8 @@ def test_substructure_combination_build(self):
         for i, ec_product in enumerate(ec_products):
             substructure_subset = db.select_substructures(ec_product, "substructures")
             smis = substructure_combination_build(substructure_subset, configs_iso,
-                                                  prescribed_structure=False, isomeric_smiles=True)
+                                                  prescribed_structure=False, isomeric_smiles=True,
+                                                  bond_enthalpies=get_bond_enthalpies())
 
             self.assertEqual(len(smis.keys()), lens[i])
 
@@ -440,7 +441,7 @@ def test_add_bonds(self):
         mol_out = [None, "*[CH]1(O)OC2**[CH](O)(C(O)C2O)[CH]1(*)O", "*C[CH](N)(C(=O)O)c1(*)ccc(O)cc1"]
 
         for i in range(len(atoms_available)):
-            mol_e = add_bonds(mol_comb[i], edges[i], atoms_available[i], bond_types[i])
+            mol_e, total_bde = add_bonds(mol_comb[i], edges[i], atoms_available[i], bond_types[i], get_bond_enthalpies())
 
             if i == 0:
                 self.assertTrue(mol_e is None)

From be844fcc94bd2b3cf9b67036bd81b4bafdc48ff4 Mon Sep 17 00:00:00 2001
From: Jack Gisby <jackgisby@gmail.com>
Date: Wed, 25 Nov 2020 19:45:38 +0000
Subject: [PATCH 19/35] Add integer MS integer IDs and implement
 calculate_frequencies to more efficiently calculate structure frequencies

---
 metaboblend/build_structures.py            | 209 ++++++++++-----------
 tests/test_build_structures.py             |  26 +--
 tests/test_data/metaboblend_queries.csv    |   8 +-
 tests/test_data/metaboblend_structures.csv |  92 ++++-----
 4 files changed, 164 insertions(+), 171 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index ea450ec..6709df3 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -282,7 +282,7 @@ def add_bonds(mols, edges, atoms_available, bond_types, bond_enthalpies):
 
         try:
             total_bde += bond_enthalpies[bond_matches[0]][mols.GetAtomWithIdx(edge[0]).GetSymbol()][mols.GetAtomWithIdx(edge[1]).GetSymbol()]
-        except SyntaxError:
+        except (SyntaxError, TypeError):
             total_bde = None
 
     return mol_edit, total_bde
@@ -321,7 +321,8 @@ def create_results_db(self):
         self.connect()
 
         self.cursor.execute("""CREATE TABLE queries (
-                                   ms_id TEXT PRIMARY KEY,
+                                   ms_id_num NUMERIC PRIMARY KEY,
+                                   ms_id TEXT,
                                    exact_mass NUMERIC,
                                    C INTEGER,
                                    H INTEGER,
@@ -340,23 +341,16 @@ def create_results_db(self):
 
         if self.msn:
             self.cursor.execute("""CREATE TABLE spectra (
-                                       ms_id TEXT,
+                                       ms_id_num NUMERIC,
                                        fragment_id NUMERIC,
                                        neutral_mass NUMERIC,
-                                       PRIMARY KEY (ms_id, fragment_id))""")
+                                       PRIMARY KEY (ms_id_num, fragment_id))""")
 
         self.cursor.execute("""CREATE TABLE structures (
-                                   ms_id TEXT,
-                                   smiles TEXT,
+                                   ms_id_num NUMERIC,
+                                   structure_smiles TEXT,
                                    frequency NUMERIC,
-                                   exact_mass NUMERIC,
-                                   C INTEGER,
-                                   H INTEGER,
-                                   N INTEGER,
-                                   O INTEGER,
-                                   P INTEGER,
-                                   S INTEGER,
-                                   PRIMARY KEY (ms_id, smiles))""")
+                                   PRIMARY KEY (ms_id_num, structure_smiles))""")
 
         self.cursor.execute("""CREATE TABLE substructures (
                                            structure_smiles TEXT ,
@@ -364,15 +358,15 @@ def create_results_db(self):
                                            PRIMARY KEY (structure_smiles, substructure_smiles))""")
 
         self.cursor.execute("""CREATE TABLE results (
-                                   ms_id TEXT,
+                                   ms_id_num NUMERIC,
                                    fragment_id NUMERIC,
                                    structure_smiles TEXT,
                                    bde NUMERIC,
-                                   PRIMARY KEY(ms_id, fragment_id, structure_smiles))""")
+                                   PRIMARY KEY(ms_id_num, fragment_id, structure_smiles))""")
 
         self.conn.commit()
 
-    def add_ms(self, msn_data, parameters):
+    def add_ms(self, msn_data, ms_id, ms_id_num, parameters):
         """
         Add entries to the `queries` and `spectra` tables.
 
@@ -383,6 +377,10 @@ def add_ms(self, msn_data, parameters):
             and fragment_masses are neutral fragment masses generated by this structure used to inform candidate
             scoring. See :py:meth:`metaboblend.build_structures.annotate_msn`.
 
+        :param ms_id: Unique identifier for the annotation of a single metabolite.
+
+        :param ms_id_num: Unique numeric identifier for the annotation of a single metaoblite.
+
         :param parameters: List of parameters, in the form: [ppm, ha_min, ha_max, max_atoms_available, max_degree,
             max_n_substructures, hydrogenation_allowance, isomeric_smiles]. See
             :py:meth:`metaboblend.build_structures.annotate_msn`.
@@ -394,36 +392,36 @@ def add_ms(self, msn_data, parameters):
             elif isinstance(parameter, bool):
                 parameters[i] = int(parameter)
 
-        for ms_id in msn_data.keys():
-
-            self.cursor.execute("""INSERT INTO queries (
-                                       ms_id,
-                                       exact_mass,
-                                       C, H, N, O, P, S,
-                                       ppm,
-                                       ha_min,
-                                       ha_max,
-                                       max_atoms_available,
-                                       max_degree,
-                                       max_n_substructures,
-                                       hydrogenation_allowance,
-                                       isomeric_smiles
-                                   ) VALUES ('{}', {}, '{}', '{}', '{}', '{}', '{}', '{}', {})""".format(
-                                       ms_id,
-                                       msn_data[ms_id]["exact_mass"],
-                                       msn_data[ms_id]["mf"][0], msn_data[ms_id]["mf"][1],
-                                       msn_data[ms_id]["mf"][2], msn_data[ms_id]["mf"][3],
-                                       msn_data[ms_id]["mf"][4], msn_data[ms_id]["mf"][5],
-                                       ", ".join([str(p) for p in parameters])
-                                   ))
+        self.cursor.execute("""INSERT INTO queries (
+                                   ms_id,
+                                   ms_id_num,
+                                   exact_mass,
+                                   C, H, N, O, P, S,
+                                   ppm,
+                                   ha_min,
+                                   ha_max,
+                                   max_atoms_available,
+                                   max_degree,
+                                   max_n_substructures,
+                                   hydrogenation_allowance,
+                                   isomeric_smiles
+                               ) VALUES ('{}', {}, {}, '{}', '{}', '{}', '{}', '{}', '{}', {})""".format(
+                                   ms_id,
+                                   ms_id_num,
+                                   msn_data[ms_id]["exact_mass"],
+                                   msn_data[ms_id]["mf"][0], msn_data[ms_id]["mf"][1],
+                                   msn_data[ms_id]["mf"][2], msn_data[ms_id]["mf"][3],
+                                   msn_data[ms_id]["mf"][4], msn_data[ms_id]["mf"][5],
+                                   ", ".join([str(p) for p in parameters])
+                               ))
 
         self.conn.commit()
 
-    def add_results(self, ms_id, smi_dict, fragment_mass=None, fragment_id=None):
+    def add_results(self, ms_id_num, smi_dict, fragment_mass=None, fragment_id=None):
         """
         Record which smiles were generated for a given fragment mass.
 
-        :param ms_id: Unique identifier for the annotation of a single metabolite.
+        :param ms_id_num: Unique identifier for the annotation of a single metabolite.
 
         :param smi_dict: The fragment and substructure smiles generated by the annotation of a single peak for a single
             metabolite.
@@ -435,11 +433,11 @@ def add_results(self, ms_id, smi_dict, fragment_mass=None, fragment_id=None):
 
         if self.msn:
             self.cursor.execute("""INSERT OR IGNORE INTO spectra (
-                                                       ms_id,
+                                                       ms_id_num,
                                                        fragment_id,
                                                        neutral_mass
                                                    ) VALUES ('{}', {}, {})""".format(
-                                                       ms_id,
+                                                       ms_id_num,
                                                        fragment_id,
                                                        fragment_mass
                                                    ))
@@ -447,13 +445,13 @@ def add_results(self, ms_id, smi_dict, fragment_mass=None, fragment_id=None):
             fragment_id = "NULL"
 
         for structure_smiles in smi_dict.keys():
-            self.cursor.execute("""INSERT INTO results (
-                                       ms_id,
+            self.cursor.execute("""INSERT OR IGNORE INTO results (
+                                       ms_id_num,
                                        fragment_id,
                                        structure_smiles,
                                        bde
                                    ) VALUES ('{}', '{}', '{}', '{}')""".format(
-                                       ms_id,
+                                       ms_id_num,
                                        fragment_id,
                                        structure_smiles,
                                        smi_dict[structure_smiles][0]
@@ -461,7 +459,7 @@ def add_results(self, ms_id, smi_dict, fragment_mass=None, fragment_id=None):
 
             for substructure_smiles in smi_dict[structure_smiles][1]:
 
-                self.cursor.execute("""INSERT INTO substructures (
+                self.cursor.execute("""INSERT OR IGNORE INTO substructures (
                                                    structure_smiles,
                                                    substructure_smiles
                                                ) VALUES ('{}', '{}')""".format(
@@ -471,56 +469,44 @@ def add_results(self, ms_id, smi_dict, fragment_mass=None, fragment_id=None):
 
         self.conn.commit()
 
-    def get_structures(self, ms_id):
+    def calculate_frequencies(self, ms_id_num):
+        """
+        Calculates structure frequencies in the SQLite DB.
+
+        :param ms_id_num: Unique identifier for the annotation of a single metabolite.
+        """
+
+        self.cursor.execute("""INSERT INTO structures (ms_id_num, structure_smiles, frequency) 
+                                   SELECT ms_id_num, structure_smiles, COUNT(*)
+                                   FROM results 
+                                   WHERE ms_id_num = {}
+                                   GROUP BY structure_smiles""".format(ms_id_num))
+
+    def get_structures(self, ms_id_num):
         """
         Gets smiles of generated structures. In the case of the MSn annotation workflow, also gets structure
         frequencies.
 
-        :param ms_id: Unique identifier for the annotation of a single metabolite.
+        :param ms_id_num: Unique identifier for the annotation of a single metabolite.
 
         :return: In the case of simple structure generation, returns a set of smiles strings for output structures.
             For the MSn annotation workflow, returns a dictionary with smiles as keys and the number of peaks for which
             the smiles were generated as values.
         """
 
-        structure_frequencies = {}
-        self.cursor.execute("SELECT DISTINCT structure_smiles FROM results WHERE ms_id = '%s'" % ms_id)
-
-        for structure_smiles in self.cursor.fetchall():
-            structure_mol = Chem.MolFromSmiles(structure_smiles[0])
-            structure_mass = calculate_exact_mass(structure_mol)
-            structure_mc = get_elements(structure_mol)
-
-            if self.msn:
-                self.cursor.execute("""SELECT DISTINCT fragment_id FROM results 
-                                           WHERE ms_id = '{}' 
-                                           AND structure_smiles = '{}'""".format(ms_id, structure_smiles[0]))
-
-                structure_frequency = len(self.cursor.fetchall())
-            else:
-                structure_frequency = "NULL"
-
-            self.cursor.execute("""INSERT INTO structures (
-                                       ms_id,
-                                       smiles,
-                                       exact_mass,
-                                       C, H, N, O, P, S,
-                                       frequency
-                                   ) VALUES ('{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')""".format(
-                                       ms_id,
-                                       structure_smiles[0],
-                                       structure_mass,
-                                       structure_mc["C"], structure_mc["H"], structure_mc["N"],
-                                       structure_mc["O"], structure_mc["P"], structure_mc["S"],
-                                       structure_frequency
-                                   ))
+        if self.msn:
+            msn_str = ", frequency"
+        else:
+            msn_str = ""
 
-            structure_frequencies[structure_smiles[0]] = structure_frequency
+        self.cursor.execute("""SELECT structure_smiles{} FROM structures 
+                                   WHERE ms_id_num = {}
+                            """.format(msn_str, ms_id_num))
 
         if self.msn:
-            return structure_frequencies
+            return [t for t in self.cursor.fetchall()]
         else:
-            return set(structure_frequencies.keys())
+            return [item for t in self.cursor.fetchall() for item in t]
 
     def generate_csv_output(self):
         """
@@ -558,7 +544,6 @@ def close(self):
 def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                  path_substructure_db: Union[str, bytes, os.PathLike] = os.path.realpath(os.getcwd()),
                  path_out: Union[str, bytes, os.PathLike] = "",
-                 write_csv_output: bool = False,
                  ppm: int = 5,
                  ha_min: Union[int, None] = None,
                  ha_max: Union[int, None] = None,
@@ -569,8 +554,9 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                  ncpus: Union[int, None] = None,
                  minimum_frequency: Union[int, None] = None,
                  hydrogenation_allowance: int = 2,
-                 yield_smi_dict: bool = True,
-                 isomeric_smiles: bool = False
+                 yield_smis: bool = True,
+                 isomeric_smiles: bool = False,
+                 write_csv_output: bool = True
                  ) -> Dict[str, Sequence[Dict[str, int]]]:
     """
     Generate molecules of a given mass using chemical substructures, connectivity graphs and spectral trees or
@@ -627,11 +613,13 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
         `hydrogenation_allowance = 1` then, to find candidate fragment substructures, we use as query the masses
         `[141.5938 - 1.007825, 141.5938, 141.5938 + 1.007825]`.
 
-    :param yield_smi_dict: If True, for each input molecule the function yields a dictionary whose keys are SMILEs
-        strings and values are the number of `fragment_masses` by which the structure was generated. Else, returns None.
+    :param yield_smis: If True, for each input molecule the function yields SMILEs the number of `fragment_masses` by
+        which the structure was generated. Else, returns None.
 
     :param isomeric_smiles:  If True, writes smiles with non-structural isomeric information.
 
+    :param write_csv_output: Whether to extract results from the SQLite3 database for deposition in CSV files.
+
     :return: For each input molecule yields a dictionary whose keys are SMILEs strings for the generated
         structures and values are the number of `fragment_masses` by which the structure was built (unless
         `yield_smi_dict = False`).
@@ -658,11 +646,11 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
         max_mass=round(max([msn_data[ms_id]["exact_mass"] for ms_id in msn_data.keys()]))
     )
 
-    results_db.add_ms(msn_data, [ppm, ha_min, ha_max, max_atoms_available, max_degree, max_n_substructures,
-                                 hydrogenation_allowance, isomeric_smiles])
-
     for i, ms_id in enumerate(msn_data.keys()):
 
+        results_db.add_ms(msn_data, ms_id, i,
+                          [ppm, ha_min, ha_max, max_atoms_available, max_degree, max_n_substructures, hydrogenation_allowance, isomeric_smiles])
+
         for j, fragment_mass in enumerate(msn_data[ms_id]["fragment_masses"]):
 
             for k in range(0 - hydrogenation_allowance, hydrogenation_allowance + 1):
@@ -682,11 +670,13 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                     isomeric_smiles=isomeric_smiles
                 )
 
-                results_db.add_results(ms_id, smi_dict, fragment_mass, j)
+                results_db.add_results(i, smi_dict, fragment_mass, j)
                 fragment_smis = None
 
-        if yield_smi_dict:
-            yield {ms_id: results_db.get_structures(ms_id)}
+        results_db.calculate_frequencies(i)
+
+        if yield_smis:
+            yield {ms_id: results_db.get_structures(i)}
 
     if write_csv_output:
         results_db.generate_csv_output()
@@ -698,7 +688,6 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
 def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
                         path_substructure_db: Union[str, bytes, os.PathLike],
                         path_out: Union[str, bytes, os.PathLike] = os.path.realpath(os.getcwd()),
-                        write_csv_output: bool = False,
                         ha_min: Union[int, None] = 2,
                         ha_max: Union[int, None] = 9,
                         max_degree: int = 6,
@@ -707,8 +696,9 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
                         ncpus: Union[int, None] = None,
                         path_connectivity_db: Union[str, bytes, os.PathLike, None] = None,
                         minimum_frequency: Union[int, None] = None,
-                        yield_smi_set: bool = True,
-                        isomeric_smiles: bool = False
+                        yield_smis: bool = True,
+                        isomeric_smiles: bool = False,
+                        write_csv_output: bool = True,
                         ) -> Dict[str, Sequence[set]]:
     """
     Generate molecules of a given mass using chemical substructures and connectivity graphs. Can optionally take a
@@ -725,8 +715,7 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
     :param path_substructure_db: The path to the SQLite 3 substructure database, as generated by
         :py:meth:`metaboblend.databases.SubstructureDb`.
 
-    :param path_sql_out: The path to the SQLite 3 substructure database, as generated by
-        :py:meth:`metaboblend.databases.SubstructureDb`.
+    :param path_out: Folder to which the SQLite 3 results database and CSV outputs should be written.
 
     :param ha_min: The minimum size (number of heavy atoms) of substructures to be used to build final structures. If
         None, no limit is applied.
@@ -763,12 +752,13 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
     :param minimum_frequency: The minimum frequency of substructures in table_name; e.g. substructures have a frequency
         of 1 if they are unique. Defaults to None, in which case this filtering method is not applied.
 
-    :param yield_smi_set: If True, yields a set of unique SMILEs string for each input molecule, else returns None.
+    :param yield_smis: If True, yields a set of unique SMILEs string for each input molecule, else returns None.
 
     :param isomeric_smiles:  If True, writes smiles with non-structural isomeric information.
 
-    :return: For each input molecule, yields a set of unique SMILEs strings (unless
-        `yield_smi_set = False`).
+    :param write_csv_output: Whether to extract results from the SQLite3 database for deposition in CSV files.
+
+    :return: For each input molecule, yields unique SMILEs strings (unless `yield_smis = False`).
     """
 
     db = SubstructureDb(path_substructure_db, path_connectivity_db)
@@ -789,11 +779,13 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
         max_mass=round(max([ms_data[ms_id]["exact_mass"] for ms_id in ms_data.keys()]))
     )
 
-    results_db.add_ms(ms_data, [None, ha_min, ha_max, max_atoms_available, max_degree, max_n_substructures,
-                                None, isomeric_smiles])
-    for ms_id in ms_data.keys():
+    for i, ms_id in enumerate(ms_data.keys()):
+
+        results_db.add_ms(ms_data, ms_id, i,
+                          [None, ha_min, ha_max, max_atoms_available, max_degree, max_n_substructures, None, isomeric_smiles])
 
         ppm = None
+
         try:
             if ms_data[ms_id]["prescribed_masses"] is not None:
                 ppm = 0
@@ -814,10 +806,11 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
             isomeric_smiles=isomeric_smiles
         )
 
-        results_db.add_results(ms_id, smi_dict, ms_data[ms_id]["prescribed_masses"])
+        results_db.add_results(i, smi_dict, ms_data[ms_id]["prescribed_masses"])
+        results_db.calculate_frequencies(i)
 
-        if yield_smi_set:
-            yield {ms_id: results_db.get_structures(ms_id)}
+        if yield_smis:
+            yield {ms_id: results_db.get_structures(i)}
 
     if write_csv_output:
         results_db.generate_csv_output()
diff --git a/tests/test_build_structures.py b/tests/test_build_structures.py
index dcbab3f..6706abc 100644
--- a/tests/test_build_structures.py
+++ b/tests/test_build_structures.py
@@ -180,7 +180,7 @@ def test_generate_structures(self):  # tests vs build
                                         write_csv_output=True, path_out=self.to_test_results(),
                                         max_degree=6, max_atoms_available=2, max_n_substructures=3,
                                         path_connectivity_db=self.to_test_data("connectivity.sqlite"),
-                                        minimum_frequency=None, yield_smi_set=True, isomeric_smiles=True))
+                                        minimum_frequency=None, yield_smis=True, isomeric_smiles=True))
 
                 returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
 
@@ -193,7 +193,7 @@ def test_generate_structures(self):  # tests vs build
                     prescribed_mass=None, ppm=None, ncpus=None, table_name="substructures", isomeric_smiles=True
                 )
 
-                self.assertEqual(set(build_smis.keys()), returned_smis)
+                self.assertEqual(set(build_smis.keys()), set(returned_smis))
 
                 ms_data = {record_dict["HMDB_ID"]: {"mf": [record_dict["C"], record_dict["H"], record_dict["N"],
                                                            record_dict["O"], record_dict["P"], record_dict["S"]],
@@ -206,7 +206,7 @@ def test_generate_structures(self):  # tests vs build
                                         write_csv_output=True, path_out=self.to_test_results(),
                                         max_degree=6, max_atoms_available=2, max_n_substructures=3,
                                         path_connectivity_db=self.to_test_data("connectivity.sqlite"),
-                                        minimum_frequency=None, yield_smi_set=True, isomeric_smiles=True))
+                                        minimum_frequency=None, yield_smis=True, isomeric_smiles=True))
 
                 returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
 
@@ -220,7 +220,7 @@ def test_generate_structures(self):  # tests vs build
                     ncpus=None, table_name="substructures", clean=True, isomeric_smiles=True
                 )
 
-                self.assertEqual(set(build_smis.keys()), returned_smis)
+                self.assertEqual(set(build_smis.keys()), set(returned_smis))
 
             ms_data = {}
             for i, record_dict in enumerate(record_dicts.values()):
@@ -236,7 +236,7 @@ def test_generate_structures(self):  # tests vs build
                                     write_csv_output=True, path_out=self.to_test_results(),
                                     max_degree=6, max_atoms_available=2, max_n_substructures=3,
                                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
-                                    minimum_frequency=None, yield_smi_set=True, isomeric_smiles=True))
+                                    minimum_frequency=None, yield_smis=True, isomeric_smiles=True))
 
             for i, record_dict in enumerate(record_dicts.values()):
                 build_smis = build(
@@ -248,7 +248,7 @@ def test_generate_structures(self):  # tests vs build
                     prescribed_mass=None, ppm=None, ncpus=None, table_name="substructures", isomeric_smiles=True
                 )
 
-                self.assertEqual(set(build_smis.keys()), returned_smi_list[i][record_dict["HMDB_ID"]])
+                self.assertEqual(set(build_smis.keys()), set(returned_smi_list[i][record_dict["HMDB_ID"]]))
 
         db.close()
 
@@ -282,18 +282,18 @@ def test_annotate_msn(self):  # tests vs build_msn
                     write_csv_output=True, path_out=self.to_test_results(),
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    minimum_frequency=None, yield_smi_dict=True, isomeric_smiles=True
+                    minimum_frequency=None, yield_smis=True, isomeric_smiles=True
                 ))
 
                 returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
 
-                self.assertEqual(len([freq for freq in set(returned_smis.values()) if freq > 1]), freqs[i])
+                self.assertEqual(len([t[1] for t in returned_smis if t[1] > 1]), freqs[i])
 
                 if smis[i] is not None:
-                    self.assertEqual(set(returned_smis.keys()), smis[i])
+                    self.assertEqual(set(t[0] for t in returned_smis), smis[i])
 
                 if i == 0:
-                    self.assertEqual(returned_smis['NCCc1ccc(O)c(O)c1'], 3)
+                    self.assertEqual(returned_smis[2][1], 3)
 
             ms_data = {}
             for i, record_dict in enumerate(record_dicts.values()):
@@ -311,12 +311,12 @@ def test_annotate_msn(self):  # tests vs build_msn
                 path_out=self.to_test_results("annotate_multi"), write_csv_output=True,
                 path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                 path_substructure_db=self.to_test_data("substructures.sqlite"),
-                minimum_frequency=None, yield_smi_dict=True,
+                minimum_frequency=None, yield_smis=True,
                 isomeric_smiles=True
             ))
 
             for i, record_dict in enumerate(record_dicts.values()):
-                self.assertEqual(len(set(returned_smi_list[i][record_dict["HMDB_ID"]].keys())), overall_lens[i])
+                self.assertEqual(len(returned_smi_list[i][record_dict["HMDB_ID"]]), overall_lens[i])
         
         db.close()
 
@@ -341,7 +341,7 @@ def test_results_db(self):
             path_out=self.to_test_results("test_results_db"), write_csv_output=True,
             path_connectivity_db=self.to_test_data("connectivity.sqlite"),
             path_substructure_db=self.to_test_data("substructures.sqlite"),
-            minimum_frequency=None, yield_smi_dict=True,
+            minimum_frequency=None, yield_smis=True,
             isomeric_smiles=True
         ))
 
diff --git a/tests/test_data/metaboblend_queries.csv b/tests/test_data/metaboblend_queries.csv
index 5f6dd89..25e3afa 100644
--- a/tests/test_data/metaboblend_queries.csv
+++ b/tests/test_data/metaboblend_queries.csv
@@ -1,5 +1,5 @@
 ms_id,exact_mass,C,H,N,O,P,S,ppm,ha_min,ha_max,max_atoms_available,max_degree,max_n_substructures,hydrogenation_allowance,isomeric_smiles
-HMDB0000073,153.078979,8,11,1,2,0,0,5,,,2,6,3,2,1
-HMDB0000122,180.06339,6,12,0,6,0,0,5,,,2,6,3,2,1
-HMDB0000158,181.073894,9,11,1,3,0,0,5,,,2,6,3,2,1
-HMDB0000186,342.116215,12,22,0,11,0,0,5,,,2,6,3,2,1
+0,HMDB0000073,153.078979,8,11,1,2,0,0,5,,,2,6,3,2,1
+1,HMDB0000122,180.06339,6,12,0,6,0,0,5,,,2,6,3,2,1
+2,HMDB0000158,181.073894,9,11,1,3,0,0,5,,,2,6,3,2,1
+3,HMDB0000186,342.116215,12,22,0,11,0,0,5,,,2,6,3,2,1
diff --git a/tests/test_data/metaboblend_structures.csv b/tests/test_data/metaboblend_structures.csv
index 2958015..73cbf77 100644
--- a/tests/test_data/metaboblend_structures.csv
+++ b/tests/test_data/metaboblend_structures.csv
@@ -1,47 +1,47 @@
 ms_id,smiles,frequency,exact_mass,C,H,N,O,P,S
-HMDB0000073,NCCc1ccc(O)c(O)c1,3,153.07897899999998,8,11,1,2,0,0
-HMDB0000073,NCCc1cc(O)cc(O)c1,1,153.07897899999998,8,11,1,2,0,0
-HMDB0000073,NCCc1cc(O)ccc1O,1,153.07897899999998,8,11,1,2,0,0
-HMDB0000122,OC1C(O)[C@H](O)[C@@H](O)[C@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC1[C@H](O)C(O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC1[C@H](O)[C@@H](O)[C@@H](O)[C@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC1[C@H](O)[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC1[C@H](O)[C@H](O)[C@@H](O)[C@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC1[C@H](O)[C@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)C(O)[C@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)O[C@H](CO)C1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@@H](O)C(O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@@H](O)[C@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@H](O)C(O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@H](O)O[C@@H]1CO,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@@H](CO)O1,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@@H](O)C1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OC(O)[C@H](O)[C@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OO[C@H](CO)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1OO[C@H](CO)[C@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@@H](O)C(O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@@H](O)C(O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@@H](O)[C@@H](O)[C@@H](CO)O1,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@@H](O)[C@@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@@H](O)[C@@H](O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](CO)O1,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)C1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@@H](CO)OC1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@@H](O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)C(O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)O[C@@H]1CO,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](CO)O1,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](O)C1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@H](O)[C@@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000122,OC[C@H]1O[C@H](O)[C@H](O)[C@H](O)[C@H]1O,1,180.06338999999997,6,12,0,6,0,0
-HMDB0000158,N[C@@H](Cc1ccc(O)cc1)C(=O)O,1,181.07389399999997,9,11,1,3,0,0
-HMDB0000158,N[C@@H](Cc1cccc(O)c1)C(=O)O,1,181.07389399999997,9,11,1,3,0,0
+0,NCCc1cc(O)cc(O)c1,1
+0,NCCc1cc(O)ccc1O,1
+0,NCCc1ccc(O)c(O)c1,3
+1,OC1C(O)[C@H](O)[C@@H](O)[C@H](O)[C@H]1O,1
+1,OC1[C@H](O)C(O)[C@H](O)[C@@H](O)[C@@H]1O,1
+1,OC1[C@H](O)[C@@H](O)[C@@H](O)[C@H](O)[C@H]1O,1
+1,OC1[C@H](O)[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1
+1,OC1[C@H](O)[C@H](O)[C@@H](O)[C@H](O)[C@H]1O,1
+1,OC1[C@H](O)[C@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1
+1,OC[C@H]1OC(O)C(O)[C@H](O)[C@@H]1O,1
+1,OC[C@H]1OC(O)O[C@H](CO)C1O,1
+1,OC[C@H]1OC(O)[C@@H](O)C(O)[C@@H]1O,1
+1,OC[C@H]1OC(O)[C@@H](O)[C@@H](O)[C@@H]1O,1
+1,OC[C@H]1OC(O)[C@@H](O)[C@H](O)[C@@H]1O,1
+1,OC[C@H]1OC(O)[C@H](O)C(O)[C@@H]1O,1
+1,OC[C@H]1OC(O)[C@H](O)O[C@@H]1CO,1
+1,OC[C@H]1OC(O)[C@H](O)[C@@H](CO)O1,1
+1,OC[C@H]1OC(O)[C@H](O)[C@@H](O)C1O,1
+1,OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O,1
+1,OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@H]1O,1
+1,OC[C@H]1OC(O)[C@H](O)[C@H](O)[C@@H]1O,1
+1,OC[C@H]1OC(O)[C@H](O)[C@H](O)[C@H]1O,1
+1,OC[C@H]1OO[C@H](CO)[C@@H](O)[C@@H]1O,1
+1,OC[C@H]1OO[C@H](CO)[C@H](O)[C@@H]1O,1
+1,OC[C@H]1O[C@@H](O)C(O)[C@@H](O)[C@@H]1O,1
+1,OC[C@H]1O[C@@H](O)C(O)[C@@H](O)[C@H]1O,1
+1,OC[C@H]1O[C@@H](O)[C@@H](O)[C@@H](CO)O1,1
+1,OC[C@H]1O[C@@H](O)[C@@H](O)[C@@H](O)[C@@H]1O,1
+1,OC[C@H]1O[C@@H](O)[C@@H](O)[C@@H](O)[C@H]1O,1
+1,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](CO)O1,1
+1,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)C1O,1
+1,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1
+1,OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@H]1O,1
+1,OC[C@H]1O[C@H](O)[C@@H](CO)OC1O,1
+1,OC[C@H]1O[C@H](O)[C@@H](O)[C@@H](O)[C@@H]1O,1
+1,OC[C@H]1O[C@H](O)[C@@H](O)[C@@H](O)[C@H]1O,1
+1,OC[C@H]1O[C@H](O)[C@H](O)C(O)[C@@H]1O,1
+1,OC[C@H]1O[C@H](O)[C@H](O)O[C@@H]1CO,1
+1,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](CO)O1,1
+1,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](O)C1O,1
+1,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](O)[C@@H]1O,1
+1,OC[C@H]1O[C@H](O)[C@H](O)[C@@H](O)[C@H]1O,1
+1,OC[C@H]1O[C@H](O)[C@H](O)[C@H](O)[C@@H]1O,1
+1,OC[C@H]1O[C@H](O)[C@H](O)[C@H](O)[C@H]1O,1
+2,N[C@@H](Cc1ccc(O)cc1)C(=O)O,1
+2,N[C@@H](Cc1cccc(O)c1)C(=O)O,1

From a5d7dcb53a3e1ec1c0d4dd0caa46154a327364a1 Mon Sep 17 00:00:00 2001
From: Jack Gisby <jackgisby@gmail.com>
Date: Wed, 25 Nov 2020 20:55:34 +0000
Subject: [PATCH 20/35] Re-format get_bond_enthalpies

---
 metaboblend/build_structures.py | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 6709df3..7a3921e 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -1069,21 +1069,21 @@ def build_from_subsets(exact_subset, mf, table_name, db):
 
 def get_bond_enthalpies():
 
-    return {1.0:   {'C': {'C': 348, 'N': 305, 'O': 360, 'P': 264, 'S': 272},
-                         'N': {'C': 305, 'N': 163, 'O': 222, 'P': None, 'S': None},
-                         'O': {'C': 360, 'N': 222, 'O': 146, 'P': 335, 'S': None},
-                         'P': {'C': 264, 'N': None, 'O': 335, 'P': 201, 'S': None},
-                         'S': {'C': 272, 'N': None, 'O': None, 'P': None, 'S': 226}},
-            1.5: {'C': {'C': 837, 'N': 890, 'O': None, 'P': None, 'S': None},
-                         'N': {'C': 890, 'N': 944, 'O': None, 'P': None, 'S': None},
-                         'O': {'C': None, 'N': None, 'O': None, 'P': None, 'S': None},
-                         'P': {'C': None, 'N': None, 'O': None, 'P': None, 'S': None},
-                         'S': {'C': None, 'N': None, 'O': None, 'P': None, 'S': None}},
-            2.0:   {'C': {'C': 612, 'N': 613, 'O': 743, 'P': None, 'S': 573},
-                         'N': {'C': 613, 'N': 409, 'O': 607, 'P': None, 'S': None},
-                         'O': {'C': 743, 'N': 607, 'O': 496, 'P': 544, 'S': 522},
-                         'P': {'C': None, 'N': None, 'O': 544, 'P': None, 'S': 335},
-                         'S': {'C': 573, 'N': None, 'O': 522, 'P': 335, 'S': 425}}}
+    return {1.0: {'C': {'C':  348, 'N':  305, 'O':  360, 'P':  264, 'S':  272},
+                  'N': {'C':  305, 'N':  163, 'O':  222, 'P': None, 'S': None},
+                  'O': {'C':  360, 'N':  222, 'O':  146, 'P':  335, 'S': None},
+                  'P': {'C':  264, 'N': None, 'O':  335, 'P':  201, 'S': None},
+                  'S': {'C':  272, 'N': None, 'O': None, 'P': None, 'S':  226}},
+            1.5: {'C': {'C':  837, 'N':  890, 'O': None, 'P': None, 'S': None},
+                  'N': {'C':  890, 'N':  944, 'O': None, 'P': None, 'S': None},
+                  'O': {'C': None, 'N': None, 'O': None, 'P': None, 'S': None},
+                  'P': {'C': None, 'N': None, 'O': None, 'P': None, 'S': None},
+                  'S': {'C': None, 'N': None, 'O': None, 'P': None, 'S': None}},
+            2.0: {'C': {'C':  612, 'N':  613, 'O':  743, 'P': None, 'S':  573},
+                  'N': {'C':  613, 'N':  409, 'O':  607, 'P': None, 'S': None},
+                  'O': {'C':  743, 'N':  607, 'O':  496, 'P':  544, 'S':  522},
+                  'P': {'C': None, 'N': None, 'O':  544, 'P': None, 'S':  335},
+                  'S': {'C':  573, 'N': None, 'O':  522, 'P':  335, 'S':  425}}}
 
 
 def substructure_combination_build(substructure_subset, configs_iso, prescribed_structure, isomeric_smiles, bond_enthalpies):

From f36e1741ccbd308932f78267be1808f1022a3f6c Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Fri, 27 Nov 2020 01:22:35 +0000
Subject: [PATCH 21/35] Use integer IDs for results DB

---
 metaboblend/build_structures.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 7a3921e..e58626a 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -305,6 +305,8 @@ def __init__(self, path_results, msn=True):
 
         self.conn = None
         self.cursor = None
+        
+        self.substructure_combo_id = 0
 
     def connect(self):
         """Connects to the results database."""
@@ -321,7 +323,7 @@ def create_results_db(self):
         self.connect()
 
         self.cursor.execute("""CREATE TABLE queries (
-                                   ms_id_num NUMERIC PRIMARY KEY,
+                                   ms_id_num INTEGER PRIMARY KEY,
                                    ms_id TEXT,
                                    exact_mass NUMERIC,
                                    C INTEGER,
@@ -341,27 +343,32 @@ def create_results_db(self):
 
         if self.msn:
             self.cursor.execute("""CREATE TABLE spectra (
-                                       ms_id_num NUMERIC,
-                                       fragment_id NUMERIC,
+                                       ms_id_num INTEGER,
+                                       fragment_id INTEGER,
                                        neutral_mass NUMERIC,
                                        PRIMARY KEY (ms_id_num, fragment_id))""")
 
         self.cursor.execute("""CREATE TABLE structures (
-                                   ms_id_num NUMERIC,
+                                   ms_id_num INTEGER,
                                    structure_smiles TEXT,
-                                   frequency NUMERIC,
+                                   frequency INTEGER,
                                    PRIMARY KEY (ms_id_num, structure_smiles))""")
 
         self.cursor.execute("""CREATE TABLE substructures (
-                                           structure_smiles TEXT ,
+                                           substructure_combo_id INTEGER,
+                                           substructure_position_id INTEGER,
+                                           ms_id_num INTEGER,
+                                           structure_smiles TEXT,
+                                           fragment_id INTEGER,
                                            substructure_smiles TEXT,
-                                           PRIMARY KEY (structure_smiles, substructure_smiles))""")
+                                           bde INTEGER,
+                                           PRIMARY KEY (substructure_combo_id, substructure_position_id))""")
 
         self.cursor.execute("""CREATE TABLE results (
-                                   ms_id_num NUMERIC,
-                                   fragment_id NUMERIC,
+                                   ms_id_num INTEGER,
+                                   fragment_id INTEGER,
                                    structure_smiles TEXT,
-                                   bde NUMERIC,
+                                   bde INTEGER,
                                    PRIMARY KEY(ms_id_num, fragment_id, structure_smiles))""")
 
         self.conn.commit()

From 580219d62a8c39bc379d3cc1144a2cd40f084be7 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Fri, 27 Nov 2020 01:22:47 +0000
Subject: [PATCH 22/35] Add retain_substructures option

---
 metaboblend/build_structures.py | 88 ++++++++++++++++++++++-----------
 1 file changed, 60 insertions(+), 28 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index e58626a..1fa4539 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -424,7 +424,7 @@ def add_ms(self, msn_data, ms_id, ms_id_num, parameters):
 
         self.conn.commit()
 
-    def add_results(self, ms_id_num, smi_dict, fragment_mass=None, fragment_id=None):
+    def add_results(self, ms_id_num, smi_dict, fragment_mass=None, fragment_id=None, retain_substructures=False):
         """
         Record which smiles were generated for a given fragment mass.
 
@@ -436,6 +436,8 @@ def add_results(self, ms_id_num, smi_dict, fragment_mass=None, fragment_id=None)
         :param fragment_mass: The neutral fragment mass that has been annotated.
 
         :param fragment_id: The unique identifier for the fragment mass that has been annotated.
+
+        :param retain_substructures: If True, record substructures in the results DB.
         """
 
         if self.msn:
@@ -452,27 +454,43 @@ def add_results(self, ms_id_num, smi_dict, fragment_mass=None, fragment_id=None)
             fragment_id = "NULL"
 
         for structure_smiles in smi_dict.keys():
+
             self.cursor.execute("""INSERT OR IGNORE INTO results (
                                        ms_id_num,
                                        fragment_id,
                                        structure_smiles,
                                        bde
-                                   ) VALUES ('{}', '{}', '{}', '{}')""".format(
+                                   ) VALUES ({}, {}, '{}', {})""".format(
                                        ms_id_num,
                                        fragment_id,
                                        structure_smiles,
-                                       smi_dict[structure_smiles][0]
+                                       min(smi_dict[structure_smiles]["bdes"])
                                    ))
 
-            for substructure_smiles in smi_dict[structure_smiles][1]:
-
-                self.cursor.execute("""INSERT OR IGNORE INTO substructures (
-                                                   structure_smiles,
-                                                   substructure_smiles
-                                               ) VALUES ('{}', '{}')""".format(
-                                                   structure_smiles,
-                                                   substructure_smiles
-                                               ))
+            if retain_substructures:
+                for i in range(len(smi_dict[structure_smiles]["substructures"])):  # for each combination
+
+                    for j, substructure in enumerate(smi_dict[structure_smiles]["substructures"][i]):
+
+                        self.cursor.execute("""INSERT INTO substructures (
+                                                           substructure_combo_id,
+                                                           substructure_position_id,
+                                                           ms_id_num,
+                                                           fragment_id,
+                                                           structure_smiles,
+                                                           substructure_smiles,
+                                                           bde
+                                                       ) VALUES ({}, {}, {}, {}, '{}', '{}', {})""".format(
+                                                           self.substructure_combo_id,
+                                                           j,
+                                                           ms_id_num,
+                                                           fragment_id,
+                                                           structure_smiles,
+                                                           substructure,
+                                                           smi_dict[structure_smiles]["bdes"][i]
+                                                       ))
+
+                    self.substructure_combo_id += 1
 
         self.conn.commit()
 
@@ -563,7 +581,8 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                  hydrogenation_allowance: int = 2,
                  yield_smis: bool = True,
                  isomeric_smiles: bool = False,
-                 write_csv_output: bool = True
+                 write_csv_output: bool = True,
+                 retain_substructures: bool = False
                  ) -> Dict[str, Sequence[Dict[str, int]]]:
     """
     Generate molecules of a given mass using chemical substructures, connectivity graphs and spectral trees or
@@ -627,6 +646,8 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
 
     :param write_csv_output: Whether to extract results from the SQLite3 database for deposition in CSV files.
 
+    :param retain_substructures: Whether to record the substructures used to generate final structures.
+
     :return: For each input molecule yields a dictionary whose keys are SMILEs strings for the generated
         structures and values are the number of `fragment_masses` by which the structure was built (unless
         `yield_smi_dict = False`).
@@ -674,11 +695,12 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                     table_name=table_name,
                     ncpus=ncpus,
                     clean=False,
-                    isomeric_smiles=isomeric_smiles
+                    isomeric_smiles=isomeric_smiles,
+                    retain_substructures=retain_substructures
                 )
 
-                results_db.add_results(i, smi_dict, fragment_mass, j)
-                fragment_smis = None
+                results_db.add_results(i, smi_dict, fragment_mass, j, retain_substructures)
+                smi_dict = None
 
         results_db.calculate_frequencies(i)
 
@@ -706,6 +728,7 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
                         yield_smis: bool = True,
                         isomeric_smiles: bool = False,
                         write_csv_output: bool = True,
+                        retain_substructures: bool = False
                         ) -> Dict[str, Sequence[set]]:
     """
     Generate molecules of a given mass using chemical substructures and connectivity graphs. Can optionally take a
@@ -810,10 +833,13 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
             table_name=table_name,
             ncpus=ncpus,
             clean=False,
-            isomeric_smiles=isomeric_smiles
+            isomeric_smiles=isomeric_smiles,
+            retain_substructures=retain_substructures
         )
 
         results_db.add_results(i, smi_dict, ms_data[ms_id]["prescribed_masses"])
+        smi_dict = None
+
         results_db.calculate_frequencies(i)
 
         if yield_smis:
@@ -826,7 +852,7 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
 
 
 def build(mf, exact_mass, max_n_substructures, path_connectivity_db, path_substructure_db,
-          prescribed_mass, ppm, ncpus, table_name, clean, isomeric_smiles):
+          prescribed_mass, ppm, ncpus, table_name, clean, isomeric_smiles, retain_substructures):
     """
     Core function for generating molecules of a given mass using substructures and connectivity graphs. Can optionally
     take a "prescribed" fragment mass to further filter results; this can be used to incorporate MSn data. Final
@@ -864,6 +890,8 @@ def build(mf, exact_mass, max_n_substructures, path_connectivity_db, path_substr
 
     :param isomeric_smiles:  If True, writes smiles with non-structural isomeric information.
 
+    :param retain_substructures: Whether to record the substructures used to generate final structures.
+
     :return: Returns a set of unique SMILEs strings.
     """
 
@@ -933,7 +961,7 @@ def build(mf, exact_mass, max_n_substructures, path_connectivity_db, path_substr
         smi_dicts = pool.map(
             partial(substructure_combination_build, configs_iso=configs_iso,
                     prescribed_structure=prescribed_mass, isomeric_smiles=isomeric_smiles,
-                    bond_enthalpies=get_bond_enthalpies()),
+                    bond_enthalpies=get_bond_enthalpies(), retain_substructures=retain_substructures),
             substructure_subsets
         )
 
@@ -941,10 +969,10 @@ def build(mf, exact_mass, max_n_substructures, path_connectivity_db, path_substr
     for d in smi_dicts:
         for k in d.keys():
             try:
-                smi_dict[k][1].update(d[k][1])
+                smi_dict[k]["bdes"] += d[k]["bdes"]
 
-                if d[k][0] < smi_dict[k][0]:
-                    smi_dict[k][0] = d[k][0]
+                if retain_substructures:
+                    smi_dict[k]["substructures"] += d[k]["substructures"]
 
             except KeyError:
                 smi_dict[k] = d[k]
@@ -1093,7 +1121,8 @@ def get_bond_enthalpies():
                   'S': {'C':  573, 'N': None, 'O':  522, 'P':  335, 'S':  425}}}
 
 
-def substructure_combination_build(substructure_subset, configs_iso, prescribed_structure, isomeric_smiles, bond_enthalpies):
+def substructure_combination_build(substructure_subset, configs_iso, prescribed_structure, isomeric_smiles,
+                                   bond_enthalpies, retain_substructures):
     """
     Final stage for building molecules; takes a combination of substructures (substructure_combination) and builds them
     according to graphs in the substructure database. May be run in parallel.
@@ -1171,15 +1200,18 @@ def substructure_combination_build(substructure_subset, configs_iso, prescribed_
             except RuntimeError:
                 continue  # bad bond type violation
 
-            final_substructures = set(subs["smiles"] for subs in substructure_combination)
+            final_substructures = [subs["smiles"] for subs in substructure_combination]
 
             try:
-                smis[final_structure][1].update(final_substructures)
+                smis[final_structure]["bdes"].append(total_bde)
 
-                if total_bde < smis[final_structure][0]:
-                    smis[final_structure][0] = total_bde
+                if retain_substructures:
+                    smis[final_structure]["substructures"].append(final_substructures)
 
             except KeyError:
-                smis[final_structure] = [total_bde, final_substructures]
+                smis[final_structure] = {"bdes": [total_bde]}
+
+                if retain_substructures:
+                    smis[final_structure]["substructures"] = [final_substructures]
 
     return smis

From a9b2cfd43ecbaa1764ec526447e32dd40acfb371 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Fri, 27 Nov 2020 01:24:36 +0000
Subject: [PATCH 23/35] Make filter_hmdbid_substructures a filtered version of
 the hmdbid_substructures table

---
 metaboblend/databases.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/metaboblend/databases.py b/metaboblend/databases.py
index 2436ecf..36d7ca3 100644
--- a/metaboblend/databases.py
+++ b/metaboblend/databases.py
@@ -184,14 +184,15 @@ def filter_hmdbid_substructures(self, min_node_weight=2):
         :param min_node_weight: Minimal count of the substructure within 'hmdbid_substructures'.
         """
 
-        self.cursor.execute("DROP TABLE IF EXISTS unique_hmdbid")
         self.cursor.execute("DROP TABLE IF EXISTS filtered_hmdbid_substructures")
 
-        self.cursor.execute("CREATE TABLE unique_hmdbid AS SELECT DISTINCT hmdbid FROM compounds")
-
         self.cursor.execute("""CREATE TABLE filtered_hmdbid_substructures AS
-                                   SELECT smiles, COUNT(*) FROM hmdbid_substructures
-                                   GROUP BY smiles HAVING COUNT(*) >=%s""" % min_node_weight)
+                                    SELECT * FROM hmdbid_substructures 
+                                    WHERE substructure_id IN (
+                                        SELECT substructure_id FROM hmdbid_substructures
+                                        GROUP BY substructure_id HAVING COUNT(*) >=%s
+                                    )
+                            """ % min_node_weight)
 
     def generate_substructure_network(self, method="default", min_node_weight=2, remove_isolated=False):
         """

From 26e2c89d53633cddd1aac31cf8f755d395192284 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Fri, 27 Nov 2020 01:25:18 +0000
Subject: [PATCH 24/35] Implement the substructure network generation algorithm
 in SQLite instead of networkx

---
 metaboblend/databases.py | 153 +++++++++++++--------------------------
 1 file changed, 49 insertions(+), 104 deletions(-)

diff --git a/metaboblend/databases.py b/metaboblend/databases.py
index 36d7ca3..b691a15 100644
--- a/metaboblend/databases.py
+++ b/metaboblend/databases.py
@@ -194,123 +194,68 @@ def filter_hmdbid_substructures(self, min_node_weight=2):
                                     )
                             """ % min_node_weight)
 
-    def generate_substructure_network(self, method="default", min_node_weight=2, remove_isolated=False):
+    def generate_substructure_network(self, min_node_weight=2, return_networkx=False):
         """
         Generate networks to explore the co-occurence of substructures in the substructure database.
 
-        :param method: Which method to use for the generation of a substructure co-occurence network.
-
-            * **default** Generate a standard substructure network using the most time-efficient method. Node weights
-                represent the frequency of substructures in the substructure database whilst edge weights represent the
-                co-occurence of these substructures. See
-                :py:meth:`metaboblend.databases.default_substructure_network`.
-
-            * **extended** Alternative method for generating substructure networks that generates an intermediate
-                metabolite-substructure linkage network; returns the same network as the **default** method. See
-                :py:meth:`metaboblend.databases.extended_substructure_network`.
-
-            * **parent_structure_linkage**  Uses the **extended** method and returns the intermediate
-                metabolite-substructure linkage network; instead of weighted edges between substructures being present,
-                the substructures are connected to the metabolites by which they were generated.
-
         :param min_node_weight: Minimum frequency of substructures to be included in the network; a minimum node weight
             of 2 means that all non-unique substructures will be present.
 
-        :param remove_isolated: Isolated nodes are substructures that do not co-occur with any others.
-
-            * **True** Remove isolated nodes from the final network.
-
-            * **False** Allow isolated nodes to remain in the final network.
-
-        :return: A :py:meth:`networkx.Graph` object containing the specified network.
-        """
-
-        substructure_graph = nx.Graph()
-        self.filter_hmdbid_substructures(min_node_weight)
-
-        self.cursor.execute("SELECT * FROM unique_hmdbid")
-        unique_hmdb_ids = self.cursor.fetchall()
-
-        self.cursor.execute("SELECT * FROM filtered_hmdbid_substructures")
-        # add node for each unique substructure, weighted by count
-        for unique_substructure in self.cursor.fetchall():
-            substructure_graph.add_node(unique_substructure[0], weight=unique_substructure[1])
-
-        # generate different flavours of network
-        if method == "default":
-            substructure_graph = self.default_substructure_network(substructure_graph, unique_hmdb_ids)
-        elif method == "extended":
-            substructure_graph = self.extended_substructure_network(substructure_graph, unique_hmdb_ids,
-                                                                    include_parents=False)
-        elif method == "parent_structure_linkage":
-            substructure_graph = self.extended_substructure_network(substructure_graph, unique_hmdb_ids,
-                                                                    include_parents=True)
-
-        if remove_isolated:
-            substructure_graph.remove_nodes_from(list(nx.isolates(substructure_graph)))
-
-        return substructure_graph
+        :param return_networkx: If True, returns a :py:meth:`networkx.Graph` containing the generated network.
 
-    def extended_substructure_network(self, substructure_graph, unique_hmdb_ids, include_parents=False):
+        :return: If return_networkx, A :py:meth:`networkx.Graph` object containing the generated network.
         """
-        Extended method for substructure network generation - ie, an intermediate substructure network is generated
-        which involves substructure nodes, weighted by frequency, and metabolite nodes; unweighted edges are found
-        between substructures and metabolites, representing the structures from which substructures were generated.
-        This intermediate network can be returned, or it may be transformed into the standard substructure network
-        generated by :py:meth:`metaboblend.databases.default_substructure_network`.
-
-        :param substructure_graph: A :py:meth:`networkx.Graph` containing substructure nodes, weighted by their
-            frequencies in the substructure database. Can be passed by
-            :py:meth:`metaboblend.databases.generate_substructure_network`.
-
-        :param unique_hmdb_ids: A list containing distinct HMDB IDs from the substructure database.
-
-        :param include_parents: Which type of substructure network to return.
 
-            * **True** Return the intermediate network containing metabolite nodes.
+        self.cursor.execute("DROP TABLE IF EXISTS substructure_graph")
 
-            * **False** Return the standard substructure network.
+        self.cursor.execute("""CREATE TABLE substructure_graph (
+                                   substructure_id_1 INTEGER,
+                                   substructure_id_2 INTEGER,
+                                   weight INTEGER NOT NULL DEFAULT 0,
+                                   PRIMARY KEY (substructure_id_1, substructure_id_2)
+                               )
+                            """)
 
-        :return: A :py:meth:`networkx.Graph` object containing the specified network.
-        """
-
-        # add node for each parent structure
-        for unique_hmdb_id in unique_hmdb_ids:
-            substructure_graph.add_node(unique_hmdb_id[0])
+        self.filter_hmdbid_substructures(min_node_weight)
 
         # add edge for each linked parent structure and substructure
-        self.cursor.execute("""SELECT * FROM hmdbid_substructures WHERE smiles IN 
-                                   (SELECT smiles FROM filtered_hmdbid_substructures)""")
-        for hmdbid_substructures in self.cursor.fetchall():
-            substructure_graph.add_edge(hmdbid_substructures[0], hmdbid_substructures[1])
-
-        if not include_parents:
-            # remove parent structures and replace with linked, weighted substructures
-            for unique_hmdb_id in unique_hmdb_ids:
-                for adj1 in substructure_graph.adj[unique_hmdb_id[0]]:
-                    for adj2 in substructure_graph.adj[unique_hmdb_id[0]]:
-                        if substructure_graph.has_edge(adj1, adj2):
-                            substructure_graph[adj1][adj2]['weight'] += 1
-                        else:
-                            substructure_graph.add_edge(adj1, adj2, weight=1)
-                substructure_graph.remove_node(unique_hmdb_id[0])
-
-            # remove self-loops
-            substructure_graph.remove_edges_from(nx.selfloop_edges(substructure_graph))
-
-        return substructure_graph
-
-    def default_substructure_network(self, substructure_graph, unique_hmdb_ids):
-        """
-        Standard method for generating substructure co-occurence networks.
-
-        :param substructure_graph: A :py:meth:`networkx.Graph` containing substructure nodes, weighted by their
-            frequencies in the substructure database. Can be passed by
-            :py:meth:`metaboblend.databases.generate_substructure_network`.
-
-        :param unique_hmdb_ids: A list containing distinct HMDB IDs from the substructure database.
-
-        :return: A :py:meth:`networkx.Graph` object containing the specified network.
+        self.cursor.execute("SELECT DISTINCT hmdbid FROM filtered_hmdbid_substructures")
+
+        for hmdb_id in self.cursor.fetchall():
+            self.cursor.execute("SELECT substructure_id FROM filtered_hmdbid_substructures WHERE hmdbid = '%s'" % hmdb_id)
+            substructures = self.cursor.fetchall()
+
+            seen_substructures = []
+            for adj1 in substructures:
+                for adj2 in seen_substructures:
+                    if adj1 == adj2:
+                        continue
+
+                    self.cursor.execute("""INSERT OR IGNORE INTO substructure_graph (
+                                               substructure_id_1,
+                                               substructure_id_2
+                                           ) VALUES ({}, {})""".format(
+                                               min(adj1[0], adj2[0]),
+                                               max(adj1[0], adj2[0])
+                                           ))
+
+                    self.cursor.execute("""UPDATE substructure_graph
+                                               SET weight = weight + 1
+                                               WHERE substructure_id_1 = '{}'
+                                               AND substructure_id_2 = '{}'
+                                        """.format(
+                                               min(adj1[0], adj2[0]),
+                                               max(adj1[0], adj2[0])
+                                        ))
+
+                seen_substructures.append(adj1)
+
+        self.conn.commit()
+
+        if return_networkx:
+            return self.get_substructure_network()
+
+    def get_substructure_network(self):
         """
 
         # add edges by walking through hmdbid_substructures

From 9eaea12f2d6ea195a5ecea1ee3648291dc9ca352 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Fri, 27 Nov 2020 01:25:58 +0000
Subject: [PATCH 25/35] Add get_substructure_network function to convert
 SQLite3 substructure network to a networkx graph

---
 metaboblend/databases.py | 32 +++++++++++++++++++-------------
 1 file changed, 19 insertions(+), 13 deletions(-)

diff --git a/metaboblend/databases.py b/metaboblend/databases.py
index b691a15..165704f 100644
--- a/metaboblend/databases.py
+++ b/metaboblend/databases.py
@@ -257,21 +257,27 @@ def generate_substructure_network(self, min_node_weight=2, return_networkx=False
 
     def get_substructure_network(self):
         """
+        Converts the SQLite 3 network representation to a :py:meth:`networkx.Graph` object.
 
-        # add edges by walking through hmdbid_substructures
-        for unique_hmdb_id in unique_hmdb_ids:
-            self.cursor.execute("""SELECT * FROM hmdbid_substructures 
-                                       WHERE smiles IN (SELECT smiles FROM filtered_hmdbid_substructures) 
-                                       AND hmdbid = '%s'""" % unique_hmdb_id)
-            nodes = []
-            for substructure in self.cursor.fetchall():
-                for node in nodes:
-                    if substructure_graph.has_edge(substructure[1], node):
-                        substructure_graph[substructure[1]][node]['weight'] += 1
-                    else:
-                        substructure_graph.add_edge(substructure[1], node, weight=1)
+        :return: A :py:meth:`networkx.Graph` object containing the generated network.
+        """
+
+        substructure_graph = nx.Graph()
+
+        self.cursor.execute("""SELECT substructure_id, COUNT(*) 
+                                   FROM filtered_hmdbid_substructures
+                                   GROUP BY substructure_id
+                            """)
+
+        for substructure in self.cursor.fetchall():
+            substructure_graph.add_node(substructure[0], weight=substructure[1])
+
+        self.cursor.execute("SELECT * FROM substructure_graph")
+
+        for edge in self.cursor.fetchall():
+            substructure_graph.add_edge(edge[0], edge[1], weight=edge[2])
 
-                nodes.append(substructure[1])
+        substructure_graph.remove_nodes_from(list(nx.isolates(substructure_graph)))
 
         return substructure_graph
 

From 8a19a0e7f2124f2dcd2c71ebf40e65adeb6c0fa6 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Fri, 27 Nov 2020 01:26:25 +0000
Subject: [PATCH 26/35] Implement get_single_edge to get substructure edge
 weights without the generation of a substructure network

---
 metaboblend/databases.py | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/metaboblend/databases.py b/metaboblend/databases.py
index 165704f..152c820 100644
--- a/metaboblend/databases.py
+++ b/metaboblend/databases.py
@@ -281,6 +281,44 @@ def get_substructure_network(self):
 
         return substructure_graph
 
+    def get_single_edge(self, substructure_ids):
+        """
+        Get the edge weight corresponding to a subset of substructures
+
+        :param substructure_ids: A list of substructure IDs; weights will be obtained for the combinations.
+        """
+
+        substructure_weights = {}
+
+        for substructure_id_1 in substructure_ids:
+            for substructure_id_2 in substructure_ids:
+
+                small_id = min(substructure_id_1, substructure_id_2)
+                large_id = max(substructure_id_1, substructure_id_2)
+
+                if substructure_id_1 == substructure_id_2:
+                    edge_weight = None
+
+                else:
+                    self.cursor.execute("""SELECT COUNT(*) 
+                                               FROM hmdbid_substructures 
+                                               WHERE substructure_id = {}
+                                               AND hmdbid IN (
+                                                   SELECT hmdbid
+                                                       FROM hmdbid_substructures
+                                                       WHERE substructure_id = {}
+                                               )
+                                        """.format(substructure_id_1, substructure_id_2))
+
+                    edge_weight = self.cursor.fetchall()[0][0]
+
+                try:
+                    substructure_weights[small_id][large_id] = edge_weight
+                except KeyError:
+                    substructure_weights[small_id] = {large_id: edge_weight}
+
+        return substructure_weights
+
     def select_mass_values(self, accuracy, masses, table_name):
         """
         Gets mass values from a table of substructures. Can limit results based on mass at integer level. Used by

From 3e434324a86d82f8ba59e377bf112d5dc879eb8f Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Fri, 27 Nov 2020 01:27:02 +0000
Subject: [PATCH 27/35] Add integer substructure key

---
 metaboblend/databases.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/metaboblend/databases.py b/metaboblend/databases.py
index 152c820..03d24a8 100644
--- a/metaboblend/databases.py
+++ b/metaboblend/databases.py
@@ -506,7 +506,8 @@ def create_compound_database(self):
                                    smiles TEXT)""")
 
         self.cursor.execute("""CREATE TABLE substructures (
-                                   smiles TEXT PRIMARY KEY, 
+                                   substructure_id INTEGER PRIMARY KEY,
+                                   smiles TEXT NOT NULL UNIQUE, 
                                    heavy_atoms INTEGER,
                                    length INTEGER,
                                    exact_mass__1 INTEGER,
@@ -527,8 +528,9 @@ def create_compound_database(self):
 
         self.cursor.execute("""CREATE TABLE hmdbid_substructures (
                                    hmdbid TEXT,
-                                   smiles,
-                                   PRIMARY KEY (hmdbid, smiles))""")
+                                   substructure_id INTEGER,
+                                   PRIMARY KEY (hmdbid, substructure_id),
+                                   FOREIGN KEY (substructure_id) REFERENCES substructures(substructure_id))""")
 
     def create_indexes(self, table="substructures", selection="all"):
         """Creates indexes for the `substructures` table for use by the build method."""
@@ -1075,6 +1077,8 @@ def update_substructure_database(hmdb_path: Union[str, bytes, os.PathLike, None]
     if ha_min is None:
         ha_min = 0
 
+    substructure_id = 0
+
     for record_dict in filter_records(records, isomeric_smiles=isomeric_smiles):
         if not substructures_only:
             cursor.execute("""INSERT OR IGNORE INTO compounds (
@@ -1236,10 +1240,12 @@ def insert_substructure(lib, cursor, record_dict, substructures_only, max_atoms_
                           :mol)""", sub_smi_dict)
 
     if not substructures_only:
+        cursor.execute("SELECT substructure_id FROM substructures WHERE smiles = '%s'" % sub_smi_dict["smiles"])
+
         cursor.execute("""INSERT OR IGNORE INTO hmdbid_substructures (
                               hmdbid, 
-                              smiles) 
-                          VALUES ("%s", "%s")""" % (record_dict['HMDB_ID'], smiles_rdkit))
+                              substructure_id) 
+                          VALUES ('{}', {})""".format(record_dict['HMDB_ID'], cursor.fetchall()[0][0]))
 
 
 def create_connectivity_database(path_connectivity_db: Union[str, bytes, os.PathLike], max_n_substructures: int = 3,

From 151a189be7880e4dc91f3469258e64f9dbba4688 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Fri, 27 Nov 2020 01:49:15 +0000
Subject: [PATCH 28/35] Update unit tests

---
 tests/test_build_structures.py       |  32 ++++++++-----
 tests/test_data/substructures.sqlite | Bin 647168 -> 569344 bytes
 tests/test_databases.py              |   6 ++-
 tests/test_substructure_database.py  |  68 +++++++++++++++------------
 4 files changed, 60 insertions(+), 46 deletions(-)

diff --git a/tests/test_build_structures.py b/tests/test_build_structures.py
index 6706abc..e74f81d 100644
--- a/tests/test_build_structures.py
+++ b/tests/test_build_structures.py
@@ -71,7 +71,7 @@ def test_build(self):  # core - all other build functions rely on
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"), clean=True,
                     prescribed_mass=None, ppm=None, ncpus=None, table_name="substructures",
-                    isomeric_smiles=True
+                    isomeric_smiles=True, retain_substructures=True
                 )
 
                 j = 0
@@ -94,7 +94,8 @@ def test_build(self):  # core - all other build functions rely on
                     prescribed_mass=fragments[i], ppm=15, clean=True,
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
-                    ncpus=None, table_name="substructures", isomeric_smiles=True
+                    ncpus=None, table_name="substructures", isomeric_smiles=True,
+                    retain_substructures=False
                 )
 
                 j = 0
@@ -122,7 +123,8 @@ def test_substructure_combination_build(self):
             substructure_subset = db.select_substructures(ec_product, "substructures")
             smis = substructure_combination_build(substructure_subset, configs_iso,
                                                   prescribed_structure=False, isomeric_smiles=True,
-                                                  bond_enthalpies=get_bond_enthalpies())
+                                                  bond_enthalpies=get_bond_enthalpies(),
+                                                  retain_substructures=False)
 
             self.assertEqual(len(smis.keys()), lens[i])
 
@@ -180,7 +182,8 @@ def test_generate_structures(self):  # tests vs build
                                         write_csv_output=True, path_out=self.to_test_results(),
                                         max_degree=6, max_atoms_available=2, max_n_substructures=3,
                                         path_connectivity_db=self.to_test_data("connectivity.sqlite"),
-                                        minimum_frequency=None, yield_smis=True, isomeric_smiles=True))
+                                        minimum_frequency=None, yield_smis=True, isomeric_smiles=True,
+                                        retain_substructures=True))
 
                 returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
 
@@ -190,7 +193,8 @@ def test_generate_structures(self):  # tests vs build
                     exact_mass=record_dict["exact_mass"],
                     max_n_substructures=3, path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"), clean=True,
-                    prescribed_mass=None, ppm=None, ncpus=None, table_name="substructures", isomeric_smiles=True
+                    prescribed_mass=None, ppm=None, ncpus=None, table_name="substructures", isomeric_smiles=True,
+                    retain_substructures=True
                 )
 
                 self.assertEqual(set(build_smis.keys()), set(returned_smis))
@@ -206,7 +210,8 @@ def test_generate_structures(self):  # tests vs build
                                         write_csv_output=True, path_out=self.to_test_results(),
                                         max_degree=6, max_atoms_available=2, max_n_substructures=3,
                                         path_connectivity_db=self.to_test_data("connectivity.sqlite"),
-                                        minimum_frequency=None, yield_smis=True, isomeric_smiles=True))
+                                        minimum_frequency=None, yield_smis=True, isomeric_smiles=True,
+                                        retain_substructures=False))
 
                 returned_smis = returned_smis[0][record_dict["HMDB_ID"]]
 
@@ -214,7 +219,7 @@ def test_generate_structures(self):  # tests vs build
                     mf=[record_dict["C"], record_dict["H"], record_dict["N"],
                         record_dict["O"], record_dict["P"], record_dict["S"]],
                     exact_mass=record_dict["exact_mass"], max_n_substructures=3,
-                    prescribed_mass=fragments[i], ppm=0,
+                    prescribed_mass=fragments[i], ppm=0, retain_substructures=False,
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
                     ncpus=None, table_name="substructures", clean=True, isomeric_smiles=True
@@ -236,13 +241,14 @@ def test_generate_structures(self):  # tests vs build
                                     write_csv_output=True, path_out=self.to_test_results(),
                                     max_degree=6, max_atoms_available=2, max_n_substructures=3,
                                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
-                                    minimum_frequency=None, yield_smis=True, isomeric_smiles=True))
+                                    minimum_frequency=None, yield_smis=True, isomeric_smiles=True,
+                                    retain_substructures=False))
 
             for i, record_dict in enumerate(record_dicts.values()):
                 build_smis = build(
                     mf=[record_dict["C"], record_dict["H"], record_dict["N"],
                         record_dict["O"], record_dict["P"], record_dict["S"]],
-                    exact_mass=record_dict["exact_mass"],
+                    exact_mass=record_dict["exact_mass"], retain_substructures=False,
                     max_n_substructures=3, path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"), clean=True,
                     prescribed_mass=None, ppm=None, ncpus=None, table_name="substructures", isomeric_smiles=True
@@ -279,7 +285,7 @@ def test_annotate_msn(self):  # tests vs build_msn
                 # test standard building
                 returned_smis = list(annotate_msn(
                     ms_data, max_degree=6, max_atoms_available=2, max_n_substructures=3,
-                    write_csv_output=True, path_out=self.to_test_results(),
+                    write_csv_output=True, retain_substructures=False, path_out=self.to_test_results(),
                     path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                     path_substructure_db=self.to_test_data("substructures.sqlite"),
                     minimum_frequency=None, yield_smis=True, isomeric_smiles=True
@@ -312,7 +318,7 @@ def test_annotate_msn(self):  # tests vs build_msn
                 path_connectivity_db=self.to_test_data("connectivity.sqlite"),
                 path_substructure_db=self.to_test_data("substructures.sqlite"),
                 minimum_frequency=None, yield_smis=True,
-                isomeric_smiles=True
+                isomeric_smiles=True, retain_substructures=False
             ))
 
             for i, record_dict in enumerate(record_dicts.values()):
@@ -342,11 +348,11 @@ def test_results_db(self):
             path_connectivity_db=self.to_test_data("connectivity.sqlite"),
             path_substructure_db=self.to_test_data("substructures.sqlite"),
             minimum_frequency=None, yield_smis=True,
-            isomeric_smiles=True
+            isomeric_smiles=True, retain_substructures=True
         ))
 
         # is the sqlite database the size we expect?
-        self.assertEqual(os.path.getsize(self.to_test_results("test_results_db", "metaboblend_results.sqlite")), 61440)
+        self.assertEqual(os.path.getsize(self.to_test_results("test_results_db", "metaboblend_results.sqlite")), 53248)
 
         # are the csv files the same as the reference?
         with open(self.to_test_results("test_results_db", "metaboblend_queries.csv"), "r") as results_file, \
diff --git a/tests/test_data/substructures.sqlite b/tests/test_data/substructures.sqlite
index a7fae0a2ea3a9fcc0d22f19fceff7b6c686c69bf..612d839e5635c1368a14b6a7314e58566be79a0b 100644
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diff --git a/tests/test_databases.py b/tests/test_databases.py
index a997642..daae038 100644
--- a/tests/test_databases.py
+++ b/tests/test_databases.py
@@ -226,7 +226,7 @@ def test_create_substructure_database(self):
         test_db_cursor.execute("SELECT * FROM hmdbid_substructures")
         for i, row in enumerate(test_db_cursor.fetchall()):
             if i == 0:
-                self.assertEqual(row, ('HMDB0000073', '*:c(:*)CCN'))
+                self.assertEqual(row, ('HMDB0000073', 1))
             total_rows = i
 
         self.assertEqual(total_rows, 1292)
@@ -262,6 +262,8 @@ def test_update_substructure_database(self):  # requires create_compound_databas
                                          self.to_test_results("test_db.sqlite"), 4, 8,
                                          method="exhaustive", isomeric_smiles=True)
 
+        shutil.copyfile(self.to_test_data("substructures.sqlite"), self.to_test_results("substructures_copy.sqlite"))
+
         test_db = sqlite3.connect(self.to_test_results("test_db.sqlite"))
         test_db_cursor = test_db.cursor()
 
@@ -296,7 +298,7 @@ def test_update_substructure_database(self):  # requires create_compound_databas
         test_db_cursor.execute("SELECT * FROM hmdbid_substructures")
         for i, row in enumerate(test_db_cursor.fetchall()):
             if i == 0:
-                self.assertEqual(row, ('HMDB0000073', '*:c(:*)CCN'))
+                self.assertEqual(row, ('HMDB0000073', 1))
             total_rows = i
 
         self.assertEqual(total_rows, 1292)
diff --git a/tests/test_substructure_database.py b/tests/test_substructure_database.py
index a950e1d..b03c7a9 100644
--- a/tests/test_substructure_database.py
+++ b/tests/test_substructure_database.py
@@ -50,16 +50,16 @@ def test_init(self):
                             self.to_test_data("connectivity.sqlite"))
 
         db.cursor.execute("SELECT * FROM substructures")
-        first_row = db.cursor.fetchone()[0:17]
-        self.assertEqual(first_row, ('*:c(:*)CCN', 4, 10, 56, 56.05, 56.05002399999998, 3, 6, 1, 0, 0, 0, 2, '{3: 2}',
-                                     1, '{3: [1.5, 1.5]}', '[4, 5]'))
+        first_row = db.cursor.fetchone()[0:18]
+        self.assertEqual(first_row, (1, '*:c(:*)CCN', 4, 10, 56, 56.05, 56.05002399999998, 3, 6, 1, 0, 0, 0, 2,
+                                     '{3: 2}', 1, '{3: [1.5, 1.5]}', '[4, 5]'))
 
-        self.assertTrue(Chem.MolFromSmiles(first_row[0], False))
+        self.assertTrue(Chem.MolFromSmiles(first_row[1], False))
         self.assertEqual(len(db.cursor.fetchall()), 1235)
 
         db.cursor.execute("SELECT * FROM hmdbid_substructures")
         first_row = db.cursor.fetchone()
-        self.assertEqual(first_row, ('HMDB0000073', '*:c(:*)CCN'))
+        self.assertEqual(first_row, ('HMDB0000073', 1))
         self.assertEqual(len(db.cursor.fetchall()), 1292)
 
         db.cursor.execute("SELECT * FROM compounds")
@@ -85,43 +85,39 @@ def test_select_compounds(self):
     def test_filter_hmdbid_substructures(self):
         db = SubstructureDb(self.to_test_data("substructures.sqlite"))
         db.filter_hmdbid_substructures(2)
-        db.cursor.execute("SELECT * FROM unique_hmdbid")
-        self.assertEqual(db.cursor.fetchall(), [('HMDB0000073',), ('HMDB0000122',), ('HMDB0000158',), ('HMDB0000186',)])
 
-        db.cursor.execute("SELECT * FROM filtered_hmdbid_substructures")
-        for i, unique_substructure in enumerate(db.cursor.fetchall()):
-            self.assertLessEqual(i, 56)
-            self.assertGreaterEqual(unique_substructure[1], 2)
+        db.cursor.execute("SELECT COUNT(*) FROM filtered_hmdbid_substructures GROUP BY hmdbid")
+        for i, hmdbid_count in enumerate(db.cursor.fetchall()):
+            self.assertGreater(hmdbid_count[0], 1)
+
+        self.assertEqual(i, 3)
 
         db.close()
 
-    def test_generate_substructure_network(self):  # also tests close
+    def test_generate_substructure_network(self):  # also tests get_substructure_network, get_single_edge and close
         db = SubstructureDb(self.to_test_data("substructures.sqlite"))
-        std = db.generate_substructure_network(method="default", min_node_weight=2, remove_isolated=False)
-        extended = db.generate_substructure_network(method="extended", min_node_weight=2, remove_isolated=False)
-        parent = db.generate_substructure_network(method="parent_structure_linkage", min_node_weight=2,
-                                                  remove_isolated=False)
 
-        for s in std.nodes:
-            self.assertTrue(s in extended.nodes and s in parent.nodes)
+        self.assertEqual(db.get_single_edge([3, 4, 2]), {3: {3: None, 4: 2}, 2: {3: 1, 4: 1, 2: None}, 4: {4: None}})
 
-        db.cursor.execute("select * from unique_hmdbid")
-        edge_count = []
+        std = db.generate_substructure_network(min_node_weight=2, return_networkx=True)
+
+        db.cursor.execute("SELECT * FROM filtered_hmdbid_substructures")
         for hmdb in db.cursor.fetchall():
-            self.assertTrue(hmdb[0] in parent.nodes)
-            edge_count.append(len(parent.edges(hmdb[0])))
 
-        db.cursor.execute("select distinct smiles from filtered_hmdbid_substructures")
+            self.assertTrue(hmdb[1] in std.nodes)
+
+        db.cursor.execute("SELECT DISTINCT substructure_id FROM filtered_hmdbid_substructures")
         self.assertEqual(len(db.cursor.fetchall()), 57)
         self.assertEqual(std.number_of_nodes(), 57)
-        self.assertEqual(extended.number_of_nodes(), 57)
-        self.assertEqual(parent.number_of_nodes() - 4, 57)
 
         self.assertEqual(std.number_of_edges(), 1024)
-        self.assertEqual(extended.number_of_edges(), 1024)
 
-        self.assertEqual(parent.number_of_edges(), 114)
-        self.assertEqual(sum(edge_count), 114)
+        edge_count = []
+        db.cursor.execute("SELECT * FROM substructure_graph")
+        for edge in db.cursor.fetchall():
+            edge_count.append(std.get_edge_data(edge[0], edge[1])["weight"])
+
+        self.assertEqual(sum(edge_count), 2048)
 
         db.cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
         self.assertEqual(len(db.cursor.fetchall()), 5)
@@ -136,7 +132,7 @@ def test_generate_substructure_network(self):  # also tests close
 
         db = SubstructureDb(self.to_test_data("substructures.sqlite"))
         db.cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
-        self.assertEqual(len(db.cursor.fetchall()), 3)
+        self.assertEqual(len(db.cursor.fetchall()), 4)
 
         db.close()
 
@@ -204,14 +200,24 @@ def test_select_substructures(self):
 
         substructures = list(db.select_substructures([[4, 4, 0, 0, 0, 0]], "substructures")[0][0].values())
         self.assertEqual([item for i, item in enumerate(substructures) if i != 1],
-                         ['*C(*)Cc(:*)c:*', {1: [1.0, 1.0], 3: [1.5], 6: [1.5]}, {1: 2, 3: 1, 6: 1}, 4, 3, [0, 4, 5, 7]])
+                         ['*Cc(:*)cc:*',
+                          {1: [1.0], 2: [1.5], 5: [1.5]},
+                          {1: 1, 2: 1, 5: 1},
+                          3,
+                          3,
+                          [0, 3, 4]])
 
         self.assertEqual(len(db.select_substructures([[7, 7, 0, 0, 0, 0]], "substructures")[0]), 3)
         self.assertEqual(list(db.select_substructures([[7, 7, 0, 0, 0, 0]], "substructures")[0][0].keys()),
                          ['smiles', 'mol', 'bond_types', 'degree_atoms', 'valence', 'atoms_available', 'dummies'])
         substructures = list(db.select_substructures([[7, 7, 0, 0, 0, 0]], "substructures")[0][0].values())
         self.assertEqual([item for i, item in enumerate(substructures) if i != 1],
-                         ['*C(*)Cc1cc:*:cc1', {1: [1.0, 1.0], 5: [1.5], 7: [1.5]}, {1: 2, 5: 1, 7: 1}, 4, 3, [0, 6, 9]])
+                         ['*CCc1c:*:c(*)cc1',
+                          {1: [1.0], 4: [1.5], 6: [1.5, 1.0]},
+                          {1: 1, 4: 1, 6: 2},
+                          4,
+                          3,
+                          [0, 5, 7]])
 
         self.assertRaises(sqlite3.OperationalError,
                           lambda: db.select_substructures([[2, 5, 0, 0, 0, 0]], "substrusctures"))

From d9f1607ac17b8d3d83430ed815535eb26d6b6001 Mon Sep 17 00:00:00 2001
From: Jack Gisby <jackgisby@gmail.com>
Date: Sun, 29 Nov 2020 17:30:05 +0000
Subject: [PATCH 29/35] Add parse_ms_data function to convert user provided raw
 data into a list required for building

---
 metaboblend/build_structures.py | 72 ++++++++++++++++++++++-----------
 1 file changed, 49 insertions(+), 23 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 1fa4539..5076491 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -566,7 +566,25 @@ def close(self):
         self.conn.close()
 
 
-def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
+def parse_ms_data(ms_data):
+    """
+    Parse raw data provided by user and yield formatted input data.
+
+    :param ms_data:
+
+    :param annotate_msn:
+
+    :return: None
+    """
+
+    if isinstance(ms_data, dict):
+        for i, ms_id in enumerate(ms_data.keys()):
+            yield [i] + ms_data[ms_id]
+
+    yield None
+
+
+def annotate_msn(msn_data: Union[str, os.PathLike, Dict[str, Dict[str, Union[int, list]]]],
                  path_substructure_db: Union[str, bytes, os.PathLike] = os.path.realpath(os.getcwd()),
                  path_out: Union[str, bytes, os.PathLike] = "",
                  ppm: int = 5,
@@ -671,22 +689,23 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
         max_degree=max_degree,
         max_atoms_available=max_atoms_available,
         minimum_frequency=minimum_frequency,
-        max_mass=round(max([msn_data[ms_id]["exact_mass"] for ms_id in msn_data.keys()]))
+        max_mass=None
     )
 
-    for i, ms_id in enumerate(msn_data.keys()):
+    # 0: i, 1: ms_id, 2: mc, 3: exact_mass, 4: fragment_masses
+    for ms in parse_ms_data(msn_data):
 
-        results_db.add_ms(msn_data, ms_id, i,
+        results_db.add_ms(msn_data, ms[1], ms[0],
                           [ppm, ha_min, ha_max, max_atoms_available, max_degree, max_n_substructures, hydrogenation_allowance, isomeric_smiles])
 
-        for j, fragment_mass in enumerate(msn_data[ms_id]["fragment_masses"]):
+        for j, fragment_mass in enumerate(ms[4]):
 
             for k in range(0 - hydrogenation_allowance, hydrogenation_allowance + 1):
                 hydrogenated_fragment_mass = fragment_mass + (k * 1.007825)  # consider re-arrangements
 
                 smi_dict = build(
-                    mf=msn_data[ms_id]["mf"],
-                    exact_mass=msn_data[ms_id]["exact_mass"],
+                    mf=ms[2],
+                    exact_mass=ms[3],
                     max_n_substructures=max_n_substructures,
                     path_connectivity_db=path_connectivity_db,
                     path_substructure_db=path_substructure_db,
@@ -699,13 +718,13 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
                     retain_substructures=retain_substructures
                 )
 
-                results_db.add_results(i, smi_dict, fragment_mass, j, retain_substructures)
+                results_db.add_results(ms[0], smi_dict, fragment_mass, j, retain_substructures)
                 smi_dict = None
 
-        results_db.calculate_frequencies(i)
+        results_db.calculate_frequencies(ms[0])
 
         if yield_smis:
-            yield {ms_id: results_db.get_structures(i)}
+            yield {ms[1]: results_db.get_structures(ms[0])}
 
     if write_csv_output:
         results_db.generate_csv_output()
@@ -714,7 +733,7 @@ def annotate_msn(msn_data: Dict[str, Dict[str, Union[int, list]]],
     results_db.close()
 
 
-def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
+def generate_structures(ms_data: Union[str, os.PathLike, Dict[str, Dict[str, Union[int, None]]]],
                         path_substructure_db: Union[str, bytes, os.PathLike],
                         path_out: Union[str, bytes, os.PathLike] = os.path.realpath(os.getcwd()),
                         ha_min: Union[int, None] = 2,
@@ -809,26 +828,27 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
         max_mass=round(max([ms_data[ms_id]["exact_mass"] for ms_id in ms_data.keys()]))
     )
 
-    for i, ms_id in enumerate(ms_data.keys()):
+    # 0: i, 1: ms_id, 2: mc, 3: exact_mass, 4: prescribed_mass
+    for ms in parse_ms_data(ms_data):
 
-        results_db.add_ms(ms_data, ms_id, i,
+        results_db.add_ms(ms_data, ms[1], ms[0],
                           [None, ha_min, ha_max, max_atoms_available, max_degree, max_n_substructures, None, isomeric_smiles])
 
         ppm = None
 
         try:
-            if ms_data[ms_id]["prescribed_masses"] is not None:
+            if ms[4] is not None:
                 ppm = 0
-        except KeyError:
-            ms_data[ms_id]["prescribed_masses"] = None
+        except IndexError:
+            ms.append(None)
 
         smi_dict = build(
-            mf=ms_data[ms_id]["mf"],
-            exact_mass=ms_data[ms_id]["exact_mass"],
+            mf=ms[2],
+            exact_mass=ms[3],
             max_n_substructures=max_n_substructures,
             path_connectivity_db=path_connectivity_db,
             path_substructure_db=path_substructure_db,
-            prescribed_mass=ms_data[ms_id]["prescribed_masses"],
+            prescribed_mass=ms[4],
             ppm=ppm,
             table_name=table_name,
             ncpus=ncpus,
@@ -837,13 +857,13 @@ def generate_structures(ms_data: Dict[str, Dict[str, Union[int, None]]],
             retain_substructures=retain_substructures
         )
 
-        results_db.add_results(i, smi_dict, ms_data[ms_id]["prescribed_masses"])
+        results_db.add_results(ms[0], smi_dict, ms[4])
         smi_dict = None
 
-        results_db.calculate_frequencies(i)
+        results_db.calculate_frequencies(ms[0])
 
         if yield_smis:
-            yield {ms_id: results_db.get_structures(i)}
+            yield {ms[1]: results_db.get_structures(ms[0])}
 
     if write_csv_output:
         results_db.generate_csv_output()
@@ -1040,6 +1060,12 @@ def gen_subs_table(db, ha_min, ha_max, max_degree, max_atoms_available, max_mass
         ha_max_statement = """
                               AND heavy_atoms <= %s""" % str(ha_max)
 
+    if max_mass is None:
+        max_mass_statment = ""
+    else:
+        max_mass_statment = """
+                               exact_mass__1 < %s""" % str(max_mass)
+
     db.cursor.execute("""CREATE TABLE {} AS
                              SELECT * FROM substructures WHERE
                                  atoms_available <= {} AND
@@ -1048,7 +1074,7 @@ def gen_subs_table(db, ha_min, ha_max, max_degree, max_atoms_available, max_mass
                       """.format(table_name,
                                  max_atoms_available,
                                  max_degree,
-                                 max_mass,
+                                 max_mass_statment,
                                  freq_statement,
                                  ha_min_statement,
                                  ha_max_statement))

From a10ca660489717a2610a63a884272c8efd0f1c5a Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Mon, 30 Nov 2020 02:37:16 +0000
Subject: [PATCH 30/35] Implement msp parsing, update existing tests and spread
 functions across additional files

---
 metaboblend/algorithms.py          | 105 ++++++
 metaboblend/auxiliary.py           |   2 +-
 metaboblend/build_structures.py    | 449 ++--------------------
 metaboblend/databases.py           |  96 +----
 metaboblend/parse.py               | 281 ++++++++++++++
 metaboblend/results.py             | 301 +++++++++++++++
 tests/test_build_structures.py     |   8 +-
 tests/test_data/massbank_msp.txt   |  87 +++++
 tests/test_data/mona_msp.msp       | 580 +++++++++++++++++++++++++++++
 tests/test_databases.py            |   1 +
 tests/test_isomorphism_database.py |   1 +
 tests/test_parse.py                |  60 +++
 tests/test_suite_auxiliary.py      |   2 +
 13 files changed, 1464 insertions(+), 509 deletions(-)
 create mode 100644 metaboblend/algorithms.py
 create mode 100644 metaboblend/parse.py
 create mode 100644 metaboblend/results.py
 create mode 100644 tests/test_data/massbank_msp.txt
 create mode 100644 tests/test_data/mona_msp.msp
 create mode 100644 tests/test_parse.py

diff --git a/metaboblend/algorithms.py b/metaboblend/algorithms.py
new file mode 100644
index 0000000..624b00a
--- /dev/null
+++ b/metaboblend/algorithms.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright © 2019-2020 Ralf Weber
+#
+# This file is part of MetaboBlend.
+#
+# MetaboBlend is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# MetaboBlend is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with MetaboBlend.  If not, see <https://www.gnu.org/licenses/>.
+#
+
+import numpy
+
+
+def find_path(mass_list, sum_matrix, n, mass, max_subset_length, path=[]):
+    """
+    Recursive solution for backtracking through the dynamic programming boolean matrix. All possible subsets are found
+
+    :param mass_list: A list of masses from which to identify subsets.
+
+    :param mass: The target mass of the sum of the substructures.
+
+    :param sum_matrix: The dynamic programming boolean matrix.
+
+    :param n: The size of mass_list.
+
+    :param max_subset_length: The maximum length of subsets to return. Allows the recursive backtracking algorithm to
+        terminate early in many cases, significantly improving runtime.
+
+    :param path: List for keeping track of the current subset.
+
+    :return: Generates of lists containing the masses of valid subsets.
+    """
+
+    # base case - the path has generated a correct solution
+    if mass == 0:
+        yield sorted(path)
+        return
+
+    # stop running when we overshoot the mass
+    elif mass < 0:
+        return
+
+    # can we sum up to the target value using the remaining masses? recursive call
+    elif sum_matrix[n][mass]:
+        yield from find_path(mass_list, sum_matrix, n - 1, mass, max_subset_length, path)
+
+        if len(path) < max_subset_length:
+            path.append(mass_list[n-1])
+
+            yield from find_path(mass_list, sum_matrix, n - 1, mass - mass_list[n - 1], max_subset_length, path)
+            path.pop()
+
+
+def subset_sum(mass_list, mass, max_subset_length=3):
+    """
+    Dynamic programming implementation of subset sum. Note that, whilst this algorithm is pseudo-polynomial, the
+    backtracking algorithm for obtaining all possible subsets has exponential complexity and so remains unsuitable
+    for large input values.  This does, however, tend to perform a lot better than non-sum_matrix implementations, as
+    we're no longer doing sums multiple times and we've cut down the operations performed during the exponential portion
+    of the method.
+
+    :param mass_list: A list of masses from which to identify subsets.
+
+    :param mass: The target mass of the sum of the substructures.
+
+    :param max_subset_length: The maximum length of subsets to return. Allows the recursive backtracking algorithm to
+        terminate early in many cases, significantly improving runtime.
+
+    :return: Generates of lists containing the masses of valid subsets.
+    """
+
+    n = len(mass_list)
+
+    # initialise dynamic programming array
+    sum_matrix = numpy.ndarray([n + 1, mass + 1], bool)
+
+    # subsets can always equal 0
+    for i in range(n+1):
+        sum_matrix[i][0] = True
+
+    # empty subsets do not have non-zero sums
+    for i in range(mass):
+        sum_matrix[0][i + 1] = False
+
+    # fill in the remaining boolean matrix
+    for i in range(n):
+        for j in range(mass+1):
+            if j >= mass_list[i]:
+                sum_matrix[i + 1][j] = sum_matrix[i][j] or sum_matrix[i][j - mass_list[i]]
+            else:
+                sum_matrix[i + 1][j] = sum_matrix[i][j]
+
+    # backtrack through the matrix recursively to obtain all solutions
+    return find_path(mass_list, sum_matrix, n, mass, max_subset_length)
diff --git a/metaboblend/auxiliary.py b/metaboblend/auxiliary.py
index 52717b8..4408224 100644
--- a/metaboblend/auxiliary.py
+++ b/metaboblend/auxiliary.py
@@ -20,8 +20,8 @@
 #
 
 import itertools
-import networkx as nx
 import pylab as plt
+import networkx as nx
 
 
 def calculate_complete_multipartite_graphs(max_atoms_available, max_n_substructures):
diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 5076491..105a14a 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -20,103 +20,22 @@
 #
 
 import os
-import multiprocessing
 import copy
+import numpy
 import itertools
-from functools import partial
+import multiprocessing
 import networkx as nx
-import numpy
-import sqlite3
-import csv
+from functools import partial
 from operator import itemgetter
 from typing import Sequence, Dict, Union
 
 from rdkit import Chem
 
-from .databases import SubstructureDb, get_elements, calculate_exact_mass
-
-
-def find_path(mass_list, sum_matrix, n, mass, max_subset_length, path=[]):
-    """
-    Recursive solution for backtracking through the dynamic programming boolean matrix. All possible subsets are found
-
-    :param mass_list: A list of masses from which to identify subsets.
-
-    :param mass: The target mass of the sum of the substructures.
-
-    :param sum_matrix: The dynamic programming boolean matrix.
-
-    :param n: The size of mass_list.
-
-    :param max_subset_length: The maximum length of subsets to return. Allows the recursive backtracking algorithm to
-        terminate early in many cases, significantly improving runtime.
-
-    :param path: List for keeping track of the current subset.
-
-    :return: Generates of lists containing the masses of valid subsets.
-    """
-
-    # base case - the path has generated a correct solution
-    if mass == 0:
-        yield sorted(path)
-        return
-
-    # stop running when we overshoot the mass
-    elif mass < 0:
-        return
 
-    # can we sum up to the target value using the remaining masses? recursive call
-    elif sum_matrix[n][mass]:
-        yield from find_path(mass_list, sum_matrix, n - 1, mass, max_subset_length, path)
-
-        if len(path) < max_subset_length:
-            path.append(mass_list[n-1])
-
-            yield from find_path(mass_list, sum_matrix, n - 1, mass - mass_list[n - 1], max_subset_length, path)
-            path.pop()
-
-
-def subset_sum(mass_list, mass, max_subset_length=3):
-    """
-    Dynamic programming implementation of subset sum. Note that, whilst this algorithm is pseudo-polynomial, the
-    backtracking algorithm for obtaining all possible subsets has exponential complexity and so remains unsuitable
-    for large input values.  This does, however, tend to perform a lot better than non-sum_matrix implementations, as
-    we're no longer doing sums multiple times and we've cut down the operations performed during the exponential portion
-    of the method.
-
-    :param mass_list: A list of masses from which to identify subsets.
-
-    :param mass: The target mass of the sum of the substructures.
-
-    :param max_subset_length: The maximum length of subsets to return. Allows the recursive backtracking algorithm to
-        terminate early in many cases, significantly improving runtime.
-
-    :return: Generates of lists containing the masses of valid subsets.
-    """
-
-    n = len(mass_list)
-
-    # initialise dynamic programming array
-    sum_matrix = numpy.ndarray([n + 1, mass + 1], bool)
-
-    # subsets can always equal 0
-    for i in range(n+1):
-        sum_matrix[i][0] = True
-
-    # empty subsets do not have non-zero sums
-    for i in range(mass):
-        sum_matrix[0][i + 1] = False
-
-    # fill in the remaining boolean matrix
-    for i in range(n):
-        for j in range(mass+1):
-            if j >= mass_list[i]:
-                sum_matrix[i + 1][j] = sum_matrix[i][j] or sum_matrix[i][j - mass_list[i]]
-            else:
-                sum_matrix[i + 1][j] = sum_matrix[i][j]
-
-    # backtrack through the matrix recursively to obtain all solutions
-    return find_path(mass_list, sum_matrix, n, mass, max_subset_length)
+from .results import ResultsDb
+from .parse import parse_ms_data
+from .algorithms import subset_sum
+from .databases import SubstructureDb
 
 
 def combine_mfs(precise_mass_grp, db, table_name, accuracy):
@@ -275,11 +194,12 @@ def add_bonds(mols, edges, atoms_available, bond_types, bond_enthalpies):
             bt_start.remove(bond_matches[0])
             bt_end.remove(bond_matches[0])
 
-        try:
+        try:  # try forming the specified bond
             mol_edit.AddBond(edge[0], edge[1], rdkit_bond_types[bond_matches[0]])
         except KeyError:
             return None, None  # unknown bond type
 
+        # calculate bond dissociation energy of "formed" bonds for the structure
         try:
             total_bde += bond_enthalpies[bond_matches[0]][mols.GetAtomWithIdx(edge[0]).GetSymbol()][mols.GetAtomWithIdx(edge[1]).GetSymbol()]
         except (SyntaxError, TypeError):
@@ -288,302 +208,6 @@ def add_bonds(mols, edges, atoms_available, bond_types, bond_enthalpies):
     return mol_edit, total_bde
 
 
-class ResultsDb:
-    """
-    Methods for interacting with the SQLITE3 results database, as created by
-    :py:meth:`metaboblend.build_structures.annotate_msn`.
-
-    :param path_results: Directory to which results will be written.
-    """
-
-    def __init__(self, path_results, msn=True):
-        """Constructor method."""
-
-        self.path_results = path_results
-        self.path_results_db = os.path.join(self.path_results, "metaboblend_results.sqlite")
-        self.msn = msn
-
-        self.conn = None
-        self.cursor = None
-        
-        self.substructure_combo_id = 0
-
-    def connect(self):
-        """Connects to the results database."""
-
-        self.conn = sqlite3.connect(self.path_results_db)
-        self.cursor = self.conn.cursor()
-
-    def create_results_db(self):
-        """Generates a new results database."""
-
-        if os.path.exists(self.path_results_db):
-            os.remove(self.path_results_db)
-
-        self.connect()
-
-        self.cursor.execute("""CREATE TABLE queries (
-                                   ms_id_num INTEGER PRIMARY KEY,
-                                   ms_id TEXT,
-                                   exact_mass NUMERIC,
-                                   C INTEGER,
-                                   H INTEGER,
-                                   N INTEGER,
-                                   O INTEGER,
-                                   P INTEGER,
-                                   S INTEGER,
-                                   ppm INTEGER,
-                                   ha_min INTEGER,
-                                   ha_max INTEGER,
-                                   max_atoms_available INTEGER,
-                                   max_degree INTEGER,
-                                   max_n_substructures INTEGER,
-                                   hydrogenation_allowance INTEGER,
-                                   isomeric_smiles INTEGER)""")
-
-        if self.msn:
-            self.cursor.execute("""CREATE TABLE spectra (
-                                       ms_id_num INTEGER,
-                                       fragment_id INTEGER,
-                                       neutral_mass NUMERIC,
-                                       PRIMARY KEY (ms_id_num, fragment_id))""")
-
-        self.cursor.execute("""CREATE TABLE structures (
-                                   ms_id_num INTEGER,
-                                   structure_smiles TEXT,
-                                   frequency INTEGER,
-                                   PRIMARY KEY (ms_id_num, structure_smiles))""")
-
-        self.cursor.execute("""CREATE TABLE substructures (
-                                           substructure_combo_id INTEGER,
-                                           substructure_position_id INTEGER,
-                                           ms_id_num INTEGER,
-                                           structure_smiles TEXT,
-                                           fragment_id INTEGER,
-                                           substructure_smiles TEXT,
-                                           bde INTEGER,
-                                           PRIMARY KEY (substructure_combo_id, substructure_position_id))""")
-
-        self.cursor.execute("""CREATE TABLE results (
-                                   ms_id_num INTEGER,
-                                   fragment_id INTEGER,
-                                   structure_smiles TEXT,
-                                   bde INTEGER,
-                                   PRIMARY KEY(ms_id_num, fragment_id, structure_smiles))""")
-
-        self.conn.commit()
-
-    def add_ms(self, msn_data, ms_id, ms_id_num, parameters):
-        """
-        Add entries to the `queries` and `spectra` tables.
-
-        :param msn_data: Dictionary in the form
-            `msn_data[id] = {mf: [C, H, N, O, P, S], exact_mass: float, fragment_masses: []}`. id represents a unique
-            identifier for a given spectral tree or fragmentation spectrum, mf is a list of integers referring to the
-            molecular formula of the structure of interest, exact_mass is the mass of this molecular formula to >=4d.p.
-            and fragment_masses are neutral fragment masses generated by this structure used to inform candidate
-            scoring. See :py:meth:`metaboblend.build_structures.annotate_msn`.
-
-        :param ms_id: Unique identifier for the annotation of a single metabolite.
-
-        :param ms_id_num: Unique numeric identifier for the annotation of a single metaoblite.
-
-        :param parameters: List of parameters, in the form: [ppm, ha_min, ha_max, max_atoms_available, max_degree,
-            max_n_substructures, hydrogenation_allowance, isomeric_smiles]. See
-            :py:meth:`metaboblend.build_structures.annotate_msn`.
-        """
-
-        for i, parameter in enumerate(parameters):
-            if parameter is None:
-                parameters[i] = "NULL"
-            elif isinstance(parameter, bool):
-                parameters[i] = int(parameter)
-
-        self.cursor.execute("""INSERT INTO queries (
-                                   ms_id,
-                                   ms_id_num,
-                                   exact_mass,
-                                   C, H, N, O, P, S,
-                                   ppm,
-                                   ha_min,
-                                   ha_max,
-                                   max_atoms_available,
-                                   max_degree,
-                                   max_n_substructures,
-                                   hydrogenation_allowance,
-                                   isomeric_smiles
-                               ) VALUES ('{}', {}, {}, '{}', '{}', '{}', '{}', '{}', '{}', {})""".format(
-                                   ms_id,
-                                   ms_id_num,
-                                   msn_data[ms_id]["exact_mass"],
-                                   msn_data[ms_id]["mf"][0], msn_data[ms_id]["mf"][1],
-                                   msn_data[ms_id]["mf"][2], msn_data[ms_id]["mf"][3],
-                                   msn_data[ms_id]["mf"][4], msn_data[ms_id]["mf"][5],
-                                   ", ".join([str(p) for p in parameters])
-                               ))
-
-        self.conn.commit()
-
-    def add_results(self, ms_id_num, smi_dict, fragment_mass=None, fragment_id=None, retain_substructures=False):
-        """
-        Record which smiles were generated for a given fragment mass.
-
-        :param ms_id_num: Unique identifier for the annotation of a single metabolite.
-
-        :param smi_dict: The fragment and substructure smiles generated by the annotation of a single peak for a single
-            metabolite.
-
-        :param fragment_mass: The neutral fragment mass that has been annotated.
-
-        :param fragment_id: The unique identifier for the fragment mass that has been annotated.
-
-        :param retain_substructures: If True, record substructures in the results DB.
-        """
-
-        if self.msn:
-            self.cursor.execute("""INSERT OR IGNORE INTO spectra (
-                                                       ms_id_num,
-                                                       fragment_id,
-                                                       neutral_mass
-                                                   ) VALUES ('{}', {}, {})""".format(
-                                                       ms_id_num,
-                                                       fragment_id,
-                                                       fragment_mass
-                                                   ))
-        else:
-            fragment_id = "NULL"
-
-        for structure_smiles in smi_dict.keys():
-
-            self.cursor.execute("""INSERT OR IGNORE INTO results (
-                                       ms_id_num,
-                                       fragment_id,
-                                       structure_smiles,
-                                       bde
-                                   ) VALUES ({}, {}, '{}', {})""".format(
-                                       ms_id_num,
-                                       fragment_id,
-                                       structure_smiles,
-                                       min(smi_dict[structure_smiles]["bdes"])
-                                   ))
-
-            if retain_substructures:
-                for i in range(len(smi_dict[structure_smiles]["substructures"])):  # for each combination
-
-                    for j, substructure in enumerate(smi_dict[structure_smiles]["substructures"][i]):
-
-                        self.cursor.execute("""INSERT INTO substructures (
-                                                           substructure_combo_id,
-                                                           substructure_position_id,
-                                                           ms_id_num,
-                                                           fragment_id,
-                                                           structure_smiles,
-                                                           substructure_smiles,
-                                                           bde
-                                                       ) VALUES ({}, {}, {}, {}, '{}', '{}', {})""".format(
-                                                           self.substructure_combo_id,
-                                                           j,
-                                                           ms_id_num,
-                                                           fragment_id,
-                                                           structure_smiles,
-                                                           substructure,
-                                                           smi_dict[structure_smiles]["bdes"][i]
-                                                       ))
-
-                    self.substructure_combo_id += 1
-
-        self.conn.commit()
-
-    def calculate_frequencies(self, ms_id_num):
-        """
-        Calculates structure frequencies in the SQLite DB.
-
-        :param ms_id_num: Unique identifier for the annotation of a single metabolite.
-        """
-
-        self.cursor.execute("""INSERT INTO structures (ms_id_num, structure_smiles, frequency) 
-                                   SELECT ms_id_num, structure_smiles, COUNT(*)
-                                   FROM results 
-                                   WHERE ms_id_num = {}
-                                   GROUP BY structure_smiles""".format(ms_id_num))
-
-    def get_structures(self, ms_id_num):
-        """
-        Gets smiles of generated structures. In the case of the MSn annotation workflow, also gets structure
-        frequencies.
-
-        :param ms_id_num: Unique identifier for the annotation of a single metabolite.
-
-        :return: In the case of simple structure generation, returns a set of smiles strings for output structures.
-            For the MSn annotation workflow, returns a dictionary with smiles as keys and the number of peaks for which
-            the smiles were generated as values.
-        """
-
-        if self.msn:
-            msn_str = ", frequency"
-        else:
-            msn_str = ""
-
-        self.cursor.execute("""SELECT structure_smiles{} FROM structures 
-                                   WHERE ms_id_num = {}
-                            """.format(msn_str, ms_id_num))
-
-        if self.msn:
-            return [t for t in self.cursor.fetchall()]
-        else:
-            return [item for t in self.cursor.fetchall() for item in t]
-
-    def generate_csv_output(self):
-        """
-        Generate CSV file output for i) queries and tool parameters and ii) structures generated.
-        """
-
-        with open(os.path.join(self.path_results, "metaboblend_queries.csv"), "w", newline="") as results_file, \
-             open(os.path.join(self.path_results, "metaboblend_structures.csv"), "w", newline="") as ms_file:
-
-            results_writer = csv.writer(results_file, delimiter=",")
-            ms_writer = csv.writer(ms_file, delimiter=",")
-
-            results_writer.writerow(["ms_id", "exact_mass", "C", "H", "N", "O", "P", "S", "ppm", "ha_min", "ha_max",
-                                     "max_atoms_available", "max_degree", "max_n_substructures",
-                                     "hydrogenation_allowance", "isomeric_smiles"])
-
-            self.cursor.execute("SELECT * FROM queries")
-
-            for query in self.cursor.fetchall():
-                results_writer.writerow(query)
-
-            ms_writer.writerow(["ms_id", "smiles", "frequency", "exact_mass", "C", "H", "N", "O", "P", "S"])
-
-            self.cursor.execute("SELECT * FROM structures")
-
-            for structure in self.cursor.fetchall():
-                ms_writer.writerow(structure)
-
-    def close(self):
-        """Close the connection to the SQLITE3 database."""
-
-        self.conn.close()
-
-
-def parse_ms_data(ms_data):
-    """
-    Parse raw data provided by user and yield formatted input data.
-
-    :param ms_data:
-
-    :param annotate_msn:
-
-    :return: None
-    """
-
-    if isinstance(ms_data, dict):
-        for i, ms_id in enumerate(ms_data.keys()):
-            yield [i] + ms_data[ms_id]
-
-    yield None
-
-
 def annotate_msn(msn_data: Union[str, os.PathLike, Dict[str, Dict[str, Union[int, list]]]],
                  path_substructure_db: Union[str, bytes, os.PathLike] = os.path.realpath(os.getcwd()),
                  path_out: Union[str, bytes, os.PathLike] = "",
@@ -692,20 +316,22 @@ def annotate_msn(msn_data: Union[str, os.PathLike, Dict[str, Dict[str, Union[int
         max_mass=None
     )
 
-    # 0: i, 1: ms_id, 2: mc, 3: exact_mass, 4: fragment_masses
-    for ms in parse_ms_data(msn_data):
+    for i, ms in enumerate(parse_ms_data(msn_data)):
+        
+        if ms is None:
+            continue
 
-        results_db.add_ms(msn_data, ms[1], ms[0],
+        results_db.add_ms(msn_data, ms["ms_id"], i,
                           [ppm, ha_min, ha_max, max_atoms_available, max_degree, max_n_substructures, hydrogenation_allowance, isomeric_smiles])
 
-        for j, fragment_mass in enumerate(ms[4]):
+        for j, fragment_mass in enumerate(ms["neutral_fragment_masses"]):
 
             for k in range(0 - hydrogenation_allowance, hydrogenation_allowance + 1):
                 hydrogenated_fragment_mass = fragment_mass + (k * 1.007825)  # consider re-arrangements
 
                 smi_dict = build(
-                    mf=ms[2],
-                    exact_mass=ms[3],
+                    mf=ms["mf"],
+                    exact_mass=ms["exact_mass"],
                     max_n_substructures=max_n_substructures,
                     path_connectivity_db=path_connectivity_db,
                     path_substructure_db=path_substructure_db,
@@ -718,13 +344,13 @@ def annotate_msn(msn_data: Union[str, os.PathLike, Dict[str, Dict[str, Union[int
                     retain_substructures=retain_substructures
                 )
 
-                results_db.add_results(ms[0], smi_dict, fragment_mass, j, retain_substructures)
+                results_db.add_results(i, smi_dict, fragment_mass, j, retain_substructures)
                 smi_dict = None
 
-        results_db.calculate_frequencies(ms[0])
+        results_db.calculate_frequencies(i)
 
         if yield_smis:
-            yield {ms[1]: results_db.get_structures(ms[0])}
+            yield {ms["ms_id"]: results_db.get_structures(i)}
 
     if write_csv_output:
         results_db.generate_csv_output()
@@ -807,6 +433,8 @@ def generate_structures(ms_data: Union[str, os.PathLike, Dict[str, Dict[str, Uni
 
     :param write_csv_output: Whether to extract results from the SQLite3 database for deposition in CSV files.
 
+    :param retain_substructures: Whether to record the substructures used to generate final structures.
+
     :return: For each input molecule, yields unique SMILEs strings (unless `yield_smis = False`).
     """
 
@@ -828,27 +456,26 @@ def generate_structures(ms_data: Union[str, os.PathLike, Dict[str, Dict[str, Uni
         max_mass=round(max([ms_data[ms_id]["exact_mass"] for ms_id in ms_data.keys()]))
     )
 
-    # 0: i, 1: ms_id, 2: mc, 3: exact_mass, 4: prescribed_mass
-    for ms in parse_ms_data(ms_data):
+    for i, ms in enumerate(parse_ms_data(ms_data, False)):
 
-        results_db.add_ms(ms_data, ms[1], ms[0],
+        results_db.add_ms(ms_data, ms["ms_id"], i,
                           [None, ha_min, ha_max, max_atoms_available, max_degree, max_n_substructures, None, isomeric_smiles])
 
         ppm = None
 
         try:
-            if ms[4] is not None:
+            if ms["prescribed_mass"] is not None:
                 ppm = 0
-        except IndexError:
-            ms.append(None)
+        except KeyError:
+            ms["prescribed_mass"] = None
 
         smi_dict = build(
-            mf=ms[2],
-            exact_mass=ms[3],
+            mf=ms["mf"],
+            exact_mass=ms["exact_mass"],
             max_n_substructures=max_n_substructures,
             path_connectivity_db=path_connectivity_db,
             path_substructure_db=path_substructure_db,
-            prescribed_mass=ms[4],
+            prescribed_mass=ms["prescribed_mass"],
             ppm=ppm,
             table_name=table_name,
             ncpus=ncpus,
@@ -857,13 +484,13 @@ def generate_structures(ms_data: Union[str, os.PathLike, Dict[str, Dict[str, Uni
             retain_substructures=retain_substructures
         )
 
-        results_db.add_results(ms[0], smi_dict, ms[4])
+        results_db.add_results(i, smi_dict, ms["prescribed_mass"])
         smi_dict = None
 
-        results_db.calculate_frequencies(ms[0])
+        results_db.calculate_frequencies(i)
 
         if yield_smis:
-            yield {ms[1]: results_db.get_structures(ms[0])}
+            yield {ms["ms_id"]: results_db.get_structures(i)}
 
     if write_csv_output:
         results_db.generate_csv_output()
@@ -1064,13 +691,13 @@ def gen_subs_table(db, ha_min, ha_max, max_degree, max_atoms_available, max_mass
         max_mass_statment = ""
     else:
         max_mass_statment = """
-                               exact_mass__1 < %s""" % str(max_mass)
+                               AND exact_mass__1 < %s""" % str(max_mass)
 
     db.cursor.execute("""CREATE TABLE {} AS
-                             SELECT * FROM substructures WHERE
-                                 atoms_available <= {} AND
-                                 valence <= {} AND
-                                 exact_mass__1 < {}{}{}{}
+                             SELECT * 
+                                 FROM substructures
+                                 WHERE atoms_available <= {}
+                                 AND valence <= {}{}{}{}{}
                       """.format(table_name,
                                  max_atoms_available,
                                  max_degree,
diff --git a/metaboblend/databases.py b/metaboblend/databases.py
index 03d24a8..2c31d12 100644
--- a/metaboblend/databases.py
+++ b/metaboblend/databases.py
@@ -21,13 +21,10 @@
 
 import io
 import os
-import sys
-import subprocess
+import pickle
 import sqlite3
 import tempfile
-import pickle
-from collections import OrderedDict
-import xml.etree.ElementTree as ElementTree
+import subprocess
 import networkx as nx
 from typing import Sequence, Dict, Union
 
@@ -35,97 +32,10 @@
 from rdkit.Chem import Recap
 from rdkit.Chem import BRICS
 
+from .parse import parse_xml
 from .auxiliary import calculate_complete_multipartite_graphs, graph_to_ri, graph_info, sort_subgraphs
 
 
-def reformat_xml(source, encoding="utf8"):
-    """
-    Reformats HMDB xml files to be compatible with :py:meth:`metaboblend.databases.parse_xml`; some such files do not
-    contain a `<hmdb xmlns="http://www.hmdb.ca">` header.
-
-    :param source: Path to file to be reformatted.
-
-    :param encoding: Encoding of source file.
-
-    :return: Source file destination.
-    """
-
-    with io.open(source, "r", encoding=encoding) as xml:
-        xml_contents = xml.readlines()
-        if "hmdb" in xml_contents[1]:
-            return source
-
-        xml_contents.insert(1, "<hmdb xmlns=\"http://www.hmdb.ca\"> \n")
-
-    with io.open(source, "w", encoding=encoding) as xml:
-        xml_contents = "".join(xml_contents)
-        xml.write(xml_contents)
-        xml.write("</hmdb>")
-
-    return source
-
-
-def parse_xml(source, encoding="utf8", reformat=False):
-    """
-    Parses the contents of HMDB xml files to to extract information for the generation of substructures.
-
-    :param source: Source file destination.
-
-    :param encoding: Encoding of source file.
-
-    :param reformat: Whether to apply :py:meth:`metaboblend.databases.reformat_xml` to the XML file. Is required for
-        XML files recording single metabolites.
-
-        * **True** Add a `<hmdb xmlns="http://www.hmdb.ca">` header to the XML file before parsing.
-
-        * **False** Parse the XML file as it is (recommended if header is present).
-
-    :return: The XML file converted to a dictionary.
-    """
-
-    if reformat:
-        reformat_xml(source, encoding)
-
-    with io.open(source, "r", encoding=encoding) as inp:
-        record_out = OrderedDict()
-
-        inp.readline()
-        inp.readline()
-
-        xml_record = ""
-        path = []
-
-        for line in inp:
-            xml_record += line
-            if line == "</metabolite>\n" or line == "</drug>\n":
-
-                if sys.version_info[0] == 3:
-                    inp = io.StringIO(xml_record)
-                else:
-                    inp = io.BytesIO(xml_record.encode('utf-8').strip())
-
-                for event, elem in ElementTree.iterparse(inp, events=("start", "end")):
-                    if event == 'end':
-                        path.pop()
-
-                    if event == 'start':
-                        path.append(elem.tag)
-                        if elem.text is not None:
-                            if elem.text.replace(" ", "") != "\n":
-
-                                path_elem = ".".join(map(str, path[1:]))
-                                if path_elem in record_out:
-                                    if type(record_out[path_elem]) != list:
-                                        record_out[path_elem] = [record_out[path_elem]]
-                                    record_out[path_elem].append(elem.text)
-                                else:
-                                    record_out[path_elem] = elem.text
-
-                xml_record = ""
-                yield record_out
-                record_out = OrderedDict()
-
-
 class SubstructureDb:
     """
     Methods for interacting with the SQLITE3 substructure and connectivity databases. Provides a connection to the
diff --git a/metaboblend/parse.py b/metaboblend/parse.py
new file mode 100644
index 0000000..28aa2f2
--- /dev/null
+++ b/metaboblend/parse.py
@@ -0,0 +1,281 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright © 2019-2020 Ralf Weber
+#
+# This file is part of MetaboBlend.
+#
+# MetaboBlend is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# MetaboBlend is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with MetaboBlend.  If not, see <https://www.gnu.org/licenses/>.
+#
+
+import io
+import re
+import sys
+import copy
+import warnings
+from collections import OrderedDict
+import xml.etree.ElementTree as ElementTree
+
+
+def parse_ms_data(ms_data, msn=True):
+    """
+    Parse raw data provided by user and yield formatted input data.
+
+    :param ms_data: Dictionary containing input data or path to an MSP file.
+
+    :return: None
+    """
+
+    if isinstance(ms_data, dict):
+        for i, ms_id in enumerate(ms_data.keys()):
+
+            ms_data[ms_id]["ms_id"] = ms_id
+
+            # check if user has provided a neutralised mass or ionised mz values
+            if "neutral_fragment_masses" in ms_data[ms_id].keys() and "exact_mass" in ms_data[ms_id].keys():
+                which = "none"
+
+            elif "exact_mass" in ms_data[ms_id].keys():
+                if msn:
+                    which = "fragments"
+                else:
+                    which = "none"
+
+            elif "neutral_fragment_masses" in ms_data[ms_id].keys() or not msn:
+                which = "precursor"
+
+            else:
+                which = "both"
+
+            yield precursor_ions_to_neutral_masses(ms_data[ms_id], which)
+
+    else:
+        yield from parse_msp(ms_data)
+
+
+def precursor_ion_to_neutral_mass(mass, precursor_type):
+    """ Convert precursor ion to predicted neutral mass for substructure searching. """
+
+    # conversions
+    precursor_dict = {"[M+H]+":  1.007276}
+
+    return mass - precursor_dict[precursor_type]
+
+
+def precursor_ions_to_neutral_masses(ms_dict, which="both"):
+    """ Convert precursor ion and fragment ions to neutral. """
+
+    if which == "precursor" or which == "both":
+        ms_dict["exact_mass"] = precursor_ion_to_neutral_mass(ms_dict["precursor_mz"],
+                                                              ms_dict["precursor_type"])
+
+    if which == "fragments" or which == "both":
+
+        ms_dict["neutral_fragment_masses"] = []
+
+        for fragment_ion_mass in ms_dict["fragment_mzs"]:
+            ms_dict["neutral_fragment_masses"].append(precursor_ion_to_neutral_mass(fragment_ion_mass,
+                                                                                    ms_dict["precursor_type"]))
+
+    return ms_dict
+
+
+def parse_msp(msp_path):
+    """ Parse msp files and yield data for each compound. """
+
+    meta_parse = get_msp_regex()
+    reached_spectra = False
+
+    empty_dict = {"ms_id": None, "mf": None, "precursor_mz": None, "fragment_mzs": []}
+    entry_dict = copy.deepcopy(empty_dict)
+
+    with open(msp_path, "r") as msp_file:
+
+        for line in msp_file:
+
+            line = re.sub('^(.{2}\\$)', "", line)  # remove "XX$" from line start in massbank files
+
+            if reached_spectra:
+                if line in ["\n", "\r\n", "//\n", "//\r\n", "", "//"]:  # reached end of spectra
+
+                    yield reformat_msp_input(entry_dict)  # completed entry ready for sending to build
+
+                    entry_dict = copy.deepcopy(empty_dict)
+                    reached_spectra = False
+
+                else:  # add peak
+                    entry_dict["fragment_mzs"].append(float(line.split()[0]))
+
+            else:
+                for meta_type in meta_parse.keys():
+                    for meta_re in meta_parse[meta_type]:
+
+                        re_query = re.search(meta_re, line, re.IGNORECASE)
+
+                        if re_query:  # TODO: walrus
+                            entry_dict[meta_type] = re_query.group(1).strip()
+
+                if re.match("^Num Peaks(.*)$", line, re.IGNORECASE) or re.match("^PEAK:(.*)", line, re.IGNORECASE):
+                    reached_spectra = True  # reached line prior to spectra
+
+    if entry_dict != empty_dict:
+        yield reformat_msp_input(entry_dict)
+
+
+def reformat_msp_input(entry_dict):
+    """ Reformat input for use by build functions. """
+
+    if entry_dict["mf"] is not None:  # convert from C5H6... to [5, 6, ...]
+        entry_dict["mf"] = mc_to_list(entry_dict["mf"])
+
+    for key in ["ms_id", "mf", "precursor_mz", "precursor_type"]:  # required for the tool to function
+        if entry_dict[key] is None:
+            if key == "ms_id":
+                warnings.warn("Entry ignored from MSP file due to lack of accession in MSP file")
+            else:
+                warnings.warn("Entry " + entry_dict["ms_id"] + " removed due to lack of valid " + key + " in MSP file")
+            return None
+
+    entry_dict["precursor_mz"] = float(entry_dict["precursor_mz"])
+
+    if len(entry_dict["fragment_mzs"]) == 0:  # require a spectra to annotate
+        warnings.warn("No fragments were identified for " + entry_dict["ms_id"] + " in MSP file")
+        return None
+
+    return precursor_ions_to_neutral_masses(entry_dict)
+
+
+def mc_to_list(mc):
+    """ Convert molecular formula to list format. """
+
+    if isinstance(mc, list):
+        return mc
+
+    mc_list = [0, 0, 0, 0, 0, 0]
+    element_positions = {"C": 0, "H": 1, "N": 2, "O": 3, "P": 4, "S": 5}
+
+    # seperates out the formula into [letter, number, letter, number, ...]
+    mc = re.findall(r"[A-Z][a-z]*|\d+", re.sub("[A-Z][a-z]*(?![\da-z])", r"\g<0>1", mc))
+
+    for i, substring in enumerate(mc):
+
+        if i % 2 == 0:  # in case of letter
+            try:
+                element_position = element_positions[substring]
+            except KeyError:  # element not in C, H, N, O, P, S
+                return None
+
+        else:  # record number following the letter
+            mc_list[element_position] = int(substring)
+
+    return mc_list
+
+
+def get_msp_regex():
+    """ Dictionary of regular expressions for parsing msp metadata. """
+
+    meta_parse = {"ms_id":          ["^accession(?:=|:)(.*)$", "^DB#(?:=|:)(.*)$", "^ACCESSION:(.*)$"],  # use accession as ms_id
+                  "mf":             ["^molecular formula(?:=|:)(.*)$", "^formula:(.*)$"],
+                  "precursor_type": ["^precursor.*type(?:=|:)(.*)$", "^adduct(?:=|:)(.*)$", "^MS\$FOCUSED_ION:\s+PRECURSOR_TYPE\s+(.*)$"],
+                  "precursor_mz":   ["^precursor m/z(?:=|:)\s*(\d*[.,]?\d*)$", "^precursor.*mz(?:=|:)\s*(\d*[.,]?\d*)$", "^MS\$FOCUSED_ION:\s+PRECURSOR_M/Z\s+(\d*[.,]?\d*)$"]}
+
+    return meta_parse
+
+
+def reformat_xml(source, encoding="utf8"):
+    """
+    Reformats HMDB xml files to be compatible with :py:meth:`metaboblend.databases.parse_xml`; some such files do not
+    contain a `<hmdb xmlns="http://www.hmdb.ca">` header.
+
+    :param source: Path to file to be reformatted.
+
+    :param encoding: Encoding of source file.
+
+    :return: Source file destination.
+    """
+
+    with io.open(source, "r", encoding=encoding) as xml:
+        xml_contents = xml.readlines()
+        if "hmdb" in xml_contents[1]:
+            return source
+
+        xml_contents.insert(1, "<hmdb xmlns=\"http://www.hmdb.ca\"> \n")
+
+    with io.open(source, "w", encoding=encoding) as xml:
+        xml_contents = "".join(xml_contents)
+        xml.write(xml_contents)
+        xml.write("</hmdb>")
+
+    return source
+
+
+def parse_xml(source, encoding="utf8", reformat=False):
+    """
+    Parses the contents of HMDB xml files to to extract information for the generation of substructures.
+
+    :param source: Source file destination.
+
+    :param encoding: Encoding of source file.
+
+    :param reformat: Whether to apply :py:meth:`metaboblend.databases.reformat_xml` to the XML file. Is required for
+        XML files recording single metabolites.
+
+        * **True** Add a `<hmdb xmlns="http://www.hmdb.ca">` header to the XML file before parsing.
+
+        * **False** Parse the XML file as it is (recommended if header is present).
+
+    :return: The XML file converted to a dictionary.
+    """
+
+    if reformat:
+        reformat_xml(source, encoding)
+
+    with io.open(source, "r", encoding=encoding) as inp:
+        record_out = OrderedDict()
+
+        inp.readline()
+        inp.readline()
+
+        xml_record = ""
+        path = []
+
+        for line in inp:
+            xml_record += line
+            if line == "</metabolite>\n" or line == "</drug>\n":
+
+                if sys.version_info[0] == 3:
+                    inp = io.StringIO(xml_record)
+                else:
+                    inp = io.BytesIO(xml_record.encode('utf-8').strip())
+
+                for event, elem in ElementTree.iterparse(inp, events=("start", "end")):
+                    if event == 'end':
+                        path.pop()
+
+                    if event == 'start':
+                        path.append(elem.tag)
+                        if elem.text is not None:
+                            if elem.text.replace(" ", "") != "\n":
+
+                                path_elem = ".".join(map(str, path[1:]))
+                                if path_elem in record_out:
+                                    if type(record_out[path_elem]) != list:
+                                        record_out[path_elem] = [record_out[path_elem]]
+                                    record_out[path_elem].append(elem.text)
+                                else:
+                                    record_out[path_elem] = elem.text
+
+                xml_record = ""
+                yield record_out
+                record_out = OrderedDict()
diff --git a/metaboblend/results.py b/metaboblend/results.py
new file mode 100644
index 0000000..573ca26
--- /dev/null
+++ b/metaboblend/results.py
@@ -0,0 +1,301 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright © 2019-2020 Ralf Weber
+#
+# This file is part of MetaboBlend.
+#
+# MetaboBlend is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# MetaboBlend is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with MetaboBlend.  If not, see <https://www.gnu.org/licenses/>.
+#
+
+import os
+import csv
+import sqlite3
+
+
+class ResultsDb:
+    """
+    Methods for interacting with the SQLITE3 results database, as created by
+    :py:meth:`metaboblend.build_structures.annotate_msn`.
+
+    :param path_results: Directory to which results will be written.
+    """
+
+    def __init__(self, path_results, msn=True):
+        """Constructor method."""
+
+        self.path_results = path_results
+        self.path_results_db = os.path.join(self.path_results, "metaboblend_results.sqlite")
+        self.msn = msn
+
+        self.conn = None
+        self.cursor = None
+
+        self.substructure_combo_id = 0
+
+    def connect(self):
+        """Connects to the results database."""
+
+        self.conn = sqlite3.connect(self.path_results_db)
+        self.cursor = self.conn.cursor()
+
+    def create_results_db(self):
+        """Generates a new results database."""
+
+        if os.path.exists(self.path_results_db):
+            os.remove(self.path_results_db)
+
+        self.connect()
+
+        self.cursor.execute("""CREATE TABLE queries (
+                                   ms_id_num INTEGER PRIMARY KEY,
+                                   ms_id TEXT,
+                                   exact_mass NUMERIC,
+                                   C INTEGER,
+                                   H INTEGER,
+                                   N INTEGER,
+                                   O INTEGER,
+                                   P INTEGER,
+                                   S INTEGER,
+                                   ppm INTEGER,
+                                   ha_min INTEGER,
+                                   ha_max INTEGER,
+                                   max_atoms_available INTEGER,
+                                   max_degree INTEGER,
+                                   max_n_substructures INTEGER,
+                                   hydrogenation_allowance INTEGER,
+                                   isomeric_smiles INTEGER)""")
+
+        if self.msn:
+            self.cursor.execute("""CREATE TABLE spectra (
+                                       ms_id_num INTEGER,
+                                       fragment_id INTEGER,
+                                       neutral_mass NUMERIC,
+                                       PRIMARY KEY (ms_id_num, fragment_id))""")
+
+        self.cursor.execute("""CREATE TABLE structures (
+                                   ms_id_num INTEGER,
+                                   structure_smiles TEXT,
+                                   frequency INTEGER,
+                                   PRIMARY KEY (ms_id_num, structure_smiles))""")
+
+        self.cursor.execute("""CREATE TABLE substructures (
+                                           substructure_combo_id INTEGER,
+                                           substructure_position_id INTEGER,
+                                           ms_id_num INTEGER,
+                                           structure_smiles TEXT,
+                                           fragment_id INTEGER,
+                                           substructure_smiles TEXT,
+                                           bde INTEGER,
+                                           PRIMARY KEY (substructure_combo_id, substructure_position_id))""")
+
+        self.cursor.execute("""CREATE TABLE results (
+                                   ms_id_num INTEGER,
+                                   fragment_id INTEGER,
+                                   structure_smiles TEXT,
+                                   bde INTEGER,
+                                   PRIMARY KEY(ms_id_num, fragment_id, structure_smiles))""")
+
+        self.conn.commit()
+
+    def add_ms(self, msn_data, ms_id, ms_id_num, parameters):
+        """
+        Add entries to the `queries` and `spectra` tables.
+
+        :param msn_data: Dictionary in the form
+            `msn_data[id] = {mf: [C, H, N, O, P, S], exact_mass: float, fragment_masses: []}`. id represents a unique
+            identifier for a given spectral tree or fragmentation spectrum, mf is a list of integers referring to the
+            molecular formula of the structure of interest, exact_mass is the mass of this molecular formula to >=4d.p.
+            and fragment_masses are neutral fragment masses generated by this structure used to inform candidate
+            scoring. See :py:meth:`metaboblend.build_structures.annotate_msn`.
+
+        :param ms_id: Unique identifier for the annotation of a single metabolite.
+
+        :param ms_id_num: Unique numeric identifier for the annotation of a single metaoblite.
+
+        :param parameters: List of parameters, in the form: [ppm, ha_min, ha_max, max_atoms_available, max_degree,
+            max_n_substructures, hydrogenation_allowance, isomeric_smiles]. See
+            :py:meth:`metaboblend.build_structures.annotate_msn`.
+        """
+
+        for i, parameter in enumerate(parameters):
+            if parameter is None:
+                parameters[i] = "NULL"
+            elif isinstance(parameter, bool):
+                parameters[i] = int(parameter)
+
+        self.cursor.execute("""INSERT INTO queries (
+                                   ms_id,
+                                   ms_id_num,
+                                   exact_mass,
+                                   C, H, N, O, P, S,
+                                   ppm,
+                                   ha_min,
+                                   ha_max,
+                                   max_atoms_available,
+                                   max_degree,
+                                   max_n_substructures,
+                                   hydrogenation_allowance,
+                                   isomeric_smiles
+                               ) VALUES ('{}', {}, {}, '{}', '{}', '{}', '{}', '{}', '{}', {})""".format(
+            ms_id,
+            ms_id_num,
+            msn_data[ms_id]["exact_mass"],
+            msn_data[ms_id]["mf"][0], msn_data[ms_id]["mf"][1],
+            msn_data[ms_id]["mf"][2], msn_data[ms_id]["mf"][3],
+            msn_data[ms_id]["mf"][4], msn_data[ms_id]["mf"][5],
+            ", ".join([str(p) for p in parameters])
+        ))
+
+        self.conn.commit()
+
+    def add_results(self, ms_id_num, smi_dict, fragment_mass=None, fragment_id=None, retain_substructures=False):
+        """
+        Record which smiles were generated for a given fragment mass.
+
+        :param ms_id_num: Unique identifier for the annotation of a single metabolite.
+
+        :param smi_dict: The fragment and substructure smiles generated by the annotation of a single peak for a single
+            metabolite.
+
+        :param fragment_mass: The neutral fragment mass that has been annotated.
+
+        :param fragment_id: The unique identifier for the fragment mass that has been annotated.
+
+        :param retain_substructures: If True, record substructures in the results DB.
+        """
+
+        if self.msn:
+            self.cursor.execute("""INSERT OR IGNORE INTO spectra (
+                                                       ms_id_num,
+                                                       fragment_id,
+                                                       neutral_mass
+                                                   ) VALUES ('{}', {}, {})""".format(
+                ms_id_num,
+                fragment_id,
+                fragment_mass
+            ))
+        else:
+            fragment_id = "NULL"
+
+        for structure_smiles in smi_dict.keys():
+
+            self.cursor.execute("""INSERT OR IGNORE INTO results (
+                                       ms_id_num,
+                                       fragment_id,
+                                       structure_smiles,
+                                       bde
+                                   ) VALUES ({}, {}, '{}', {})""".format(
+                ms_id_num,
+                fragment_id,
+                structure_smiles,
+                min(smi_dict[structure_smiles]["bdes"])
+            ))
+
+            if retain_substructures:
+                for i in range(len(smi_dict[structure_smiles]["substructures"])):  # for each combination
+
+                    for j, substructure in enumerate(smi_dict[structure_smiles]["substructures"][i]):
+                        self.cursor.execute("""INSERT INTO substructures (
+                                                           substructure_combo_id,
+                                                           substructure_position_id,
+                                                           ms_id_num,
+                                                           fragment_id,
+                                                           structure_smiles,
+                                                           substructure_smiles,
+                                                           bde
+                                                       ) VALUES ({}, {}, {}, {}, '{}', '{}', {})""".format(
+                            self.substructure_combo_id,
+                            j,
+                            ms_id_num,
+                            fragment_id,
+                            structure_smiles,
+                            substructure,
+                            smi_dict[structure_smiles]["bdes"][i]
+                        ))
+
+                    self.substructure_combo_id += 1
+
+        self.conn.commit()
+
+    def calculate_frequencies(self, ms_id_num):
+        """
+        Calculates structure frequencies in the SQLite DB.
+
+        :param ms_id_num: Unique identifier for the annotation of a single metabolite.
+        """
+
+        self.cursor.execute("""INSERT INTO structures (ms_id_num, structure_smiles, frequency) 
+                                   SELECT ms_id_num, structure_smiles, COUNT(*)
+                                   FROM results 
+                                   WHERE ms_id_num = {}
+                                   GROUP BY structure_smiles""".format(ms_id_num))
+
+    def get_structures(self, ms_id_num):
+        """
+        Gets smiles of generated structures. In the case of the MSn annotation workflow, also gets structure
+        frequencies.
+
+        :param ms_id_num: Unique identifier for the annotation of a single metabolite.
+
+        :return: In the case of simple structure generation, returns a set of smiles strings for output structures.
+            For the MSn annotation workflow, returns a dictionary with smiles as keys and the number of peaks for which
+            the smiles were generated as values.
+        """
+
+        if self.msn:
+            msn_str = ", frequency"
+        else:
+            msn_str = ""
+
+        self.cursor.execute("""SELECT structure_smiles{} FROM structures 
+                                   WHERE ms_id_num = {}
+                            """.format(msn_str, ms_id_num))
+
+        if self.msn:
+            return [t for t in self.cursor.fetchall()]
+        else:
+            return [item for t in self.cursor.fetchall() for item in t]
+
+    def generate_csv_output(self):
+        """
+        Generate CSV file output for i) queries and tool parameters and ii) structures generated.
+        """
+
+        with open(os.path.join(self.path_results, "metaboblend_queries.csv"), "w", newline="") as results_file, \
+                open(os.path.join(self.path_results, "metaboblend_structures.csv"), "w", newline="") as ms_file:
+
+            results_writer = csv.writer(results_file, delimiter=",")
+            ms_writer = csv.writer(ms_file, delimiter=",")
+
+            results_writer.writerow(["ms_id", "exact_mass", "C", "H", "N", "O", "P", "S", "ppm", "ha_min", "ha_max",
+                                     "max_atoms_available", "max_degree", "max_n_substructures",
+                                     "hydrogenation_allowance", "isomeric_smiles"])
+
+            self.cursor.execute("SELECT * FROM queries")
+
+            for query in self.cursor.fetchall():
+                results_writer.writerow(query)
+
+            ms_writer.writerow(["ms_id", "smiles", "frequency", "exact_mass", "C", "H", "N", "O", "P", "S"])
+
+            self.cursor.execute("SELECT * FROM structures")
+
+            for structure in self.cursor.fetchall():
+                ms_writer.writerow(structure)
+
+    def close(self):
+        """Close the connection to the SQLITE3 database."""
+
+        self.conn.close()
diff --git a/tests/test_build_structures.py b/tests/test_build_structures.py
index e74f81d..32a67c7 100644
--- a/tests/test_build_structures.py
+++ b/tests/test_build_structures.py
@@ -202,7 +202,7 @@ def test_generate_structures(self):  # tests vs build
                 ms_data = {record_dict["HMDB_ID"]: {"mf": [record_dict["C"], record_dict["H"], record_dict["N"],
                                                            record_dict["O"], record_dict["P"], record_dict["S"]],
                                                     "exact_mass": record_dict["exact_mass"],
-                                                    "prescribed_masses": fragments[i]}}
+                                                    "prescribed_mass": fragments[i]}}
 
                 # test prescribed building
                 returned_smis = list(
@@ -280,7 +280,7 @@ def test_annotate_msn(self):  # tests vs build_msn
                 ms_data = {record_dict["HMDB_ID"]: {"mf": [record_dict["C"], record_dict["H"], record_dict["N"],
                                                            record_dict["O"], record_dict["P"], record_dict["S"]],
                                                     "exact_mass": record_dict["exact_mass"],
-                                                    "fragment_masses": fragments}}
+                                                    "neutral_fragment_masses": fragments}}
 
                 # test standard building
                 returned_smis = list(annotate_msn(
@@ -307,7 +307,7 @@ def test_annotate_msn(self):  # tests vs build_msn
                 ms_data[record_dict["HMDB_ID"]] = {"mf": [record_dict["C"], record_dict["H"], record_dict["N"],
                                                           record_dict["O"], record_dict["P"], record_dict["S"]],
                                                    "exact_mass": record_dict["exact_mass"],
-                                                   "fragment_masses": fragments}
+                                                   "neutral_fragment_masses": fragments}
 
             os.mkdir(self.to_test_results("annotate_multi"))
 
@@ -338,7 +338,7 @@ def test_results_db(self):
             ms_data[record_dict["HMDB_ID"]] = {"mf": [record_dict["C"], record_dict["H"], record_dict["N"],
                                                       record_dict["O"], record_dict["P"], record_dict["S"]],
                                                "exact_mass": record_dict["exact_mass"],
-                                               "fragment_masses": fragments}
+                                               "neutral_fragment_masses": fragments}
 
         os.mkdir(self.to_test_results("test_results_db"))
 
diff --git a/tests/test_data/massbank_msp.txt b/tests/test_data/massbank_msp.txt
new file mode 100644
index 0000000..cdfeb00
--- /dev/null
+++ b/tests/test_data/massbank_msp.txt
@@ -0,0 +1,87 @@
+ACCESSION: UO000002
+RECORD_TITLE: 2,3-di-O-Phytanyl-sn-glycerol-1-phosphoserine; EI-B; MS
+DATE: 2016.01.19 (Created 2009.05.29, modified 2011.05.06)
+AUTHORS: Hiroyuki Morii, Department of Chemistry, University of Occupational and Environmental Health
+LICENSE: CC BY-SA
+PUBLICATION: Morii, H., Nishihara, M., Ohga, M., and Koga, Y. 1986. A diphytanyl ether analog of phosphatidylserine from a methanogenic bacterium, Methanobrevibacter arboriphilus. J Lipid Res. 27: 724-730.
+COMMENT: Ammonium salt of the compound was analyzed
+COMMENT: [Analytical] Ionizing Curr 300 uA, Chamber Temp 250 C, Accel Volt 3KV, Ion Multi 1.0 KV,
+CH$NAME: 2,3-di-O-Phytanyl-sn-glycerol-1-phosphoserine
+CH$NAME: archaetidylserine
+CH$COMPOUND_CLASS: Glycerophospholipids; Glycerophosphoserines; Dialkylglycerophosphoserines
+CH$FORMULA: C46H94NO8P
+CH$EXACT_MASS: 819.67171
+CH$SMILES: C(CCC(C)C)C(C)CCCC(CCCC(CCOCC(OCCC(CCCC(C)CCCC(C)CCCC(C)C)C)COP(O)(=O)OCC(C(O)=O)N)C)C
+CH$IUPAC: InChI=1S/C46H94NO8P/c1-36(2)17-11-19-38(5)21-13-23-40(7)25-15-27-42(9)29-31-52-33-44(34-54-56(50,51)55-35-45(47)46(48)49)53-32-30-43(10)28-16-26-41(8)24-14-22-39(6)20-12-18-37(3)4/h36-45H,11-35,47H2,1-10H3,(H,48,49)(H,50,51)/t38-,39-,40-,41-,42-,43-,44-,45-/m1/s1
+CH$LINK: CAS 105662-26-8
+CH$LINK: LIPIDBANK EEL3026
+AC$INSTRUMENT: JMS DX-300/JMS-3500 data system, Japan Electron Optics Laboratory, Japan
+AC$INSTRUMENT_TYPE: EI-B
+AC$MASS_SPECTROMETRY: MS_TYPE MS
+AC$MASS_SPECTROMETRY: ION_MODE POSITIVE
+AC$MASS_SPECTROMETRY: IONIZATION_POTENTIAL 30 eV
+AC$MASS_SPECTROMETRY: SCANNING 5 Sec
+AC$MASS_SPECTROMETRY: SOURCE_TEMPERATURE 320 C
+MS$FOCUSED_ION: ION_TYPE [M]+*
+PK$SPLASH: splash10-05gi-9611001000-5e7663b41cf47681770e
+PK$NUM_PEAK: 58
+PK$PEAK: m/z int. rel.int.
+  36.0 48.935 69
+  43.0 155.642 221
+  55.0 174.853 248
+  56.0 241.397 343
+  57.0 685.069 973
+  69.0 429.724 610
+  70.0 442.816 629
+  71.0 703.562 999
+  74.0 153.368 218
+  81.0 183.718 261
+  82.0 116.090 165
+  83.0 432.501 614
+  84.0 189.394 269
+  85.0 524.513 745
+  95.0 102.128 145
+  96.0 179.720 255
+  97.0 449.439 638
+  98.0 118.287 168
+  99.0 376.928 535
+  111.0 429.907 610
+  112.0 168.627 239
+  113.0 308.186 438
+  123.0 298.191 423
+  124.0 233.188 331
+  125.0 504.097 716
+  126.0 362.310 514
+  127.0 300.450 427
+  139.0 123.338 175
+  140.0 233.340 331
+  141.0 235.767 335
+  153.0 107.469 153
+  155.0 163.241 232
+  169.0 130.189 185
+  182.0 94.773 135
+  183.0 160.234 228
+  196.0 132.555 188
+  197.0 163.622 232
+  211.0 75.654 107
+  278.0 326.649 464
+  279.0 220.386 313
+  280.0 227.649 323
+  281.0 143.572 204
+  296.0 158.358 225
+  297.0 60.502 86
+  309.0 35.370 50
+  325.0 279.819 397
+  326.0 71.000 101
+  340.0 133.760 190
+  341.0 60.486 86
+  343.0 243.579 346
+  344.0 60.028 85
+  354.0 51.468 73
+  373.0 132.555 188
+  374.0 42.069 60
+  383.0 63.767 91
+  634.0 450.446 640
+  635.0 212.497 302
+  636.0 49.622 70
+//
diff --git a/tests/test_data/mona_msp.msp b/tests/test_data/mona_msp.msp
new file mode 100644
index 0000000..b1f6d21
--- /dev/null
+++ b/tests/test_data/mona_msp.msp
@@ -0,0 +1,580 @@
+Name: Sulfaclozine
+Synon: 4-amino-N-(6-chloropyrazin-2-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100601
+InChIKey: QKLPUVXBJHRFQZ-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 285.0208
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: Ramp 21.1-31.6 eV
+Formula: C10H9ClN4O2S
+MW: 284
+ExactMass: 284.013474208
+Comments: "accession=AU100601" "author=Nikiforos Alygizakis, Anna Bletsou, Nikolaos Thomaidis, University of Athens" "license=CC BY" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=284.0135" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=Ramp 21.1-31.6 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.6 min" "solvent a=water with 0.01% formic acid and 5mM ammonium formate" "solvent b=90:10 methanol:water with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=285.0208" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.17469602228006656" "mass error=4.9792000027082395E-5" "SMILES=c1cc(ccc1N)S(=O)(=O)Nc2cncc(n2)Cl" "cas=102-65-8" "pubchem cid=66890" "chemspider=60252" "InChI=InChI=1S/C10H9ClN4O2S/c11-9-5-13-6-10(14-9)15-18(16,17)8-3-1-7(12)2-4-8/h1-6H,12H2,(H,14,15)" "InChIKey=QKLPUVXBJHRFQZ-UHFFFAOYSA-N" "molecular formula=C10H9ClN4O2S" "total exact mass=284.013474208" "SMILES=C=1C=C(C=CC1N)S(N=C2C=NC=C(Cl)N2)(=O)=O"
+Num Peaks: 27
+53.0389 0.594951
+54.0333 0.566811
+55.0178 0.522592
+60.0552 0.542692
+65.0382 3.822962
+66.0423 0.506512
+68.049 7.963499
+78.0333 0.727609
+79.0177 1.057244
+92.0498 7.702203
+93.0532 0.731629
+96.0443 0.623091
+108.0457 12.172375
+109.0483 1.181862
+110.0609 4.904325
+120.0562 3.095353
+130.0172 5.656054
+132.0138 1.515517
+156.0118 100.000000
+157.015 8.884065
+158.008 3.891301
+174.0228 0.751729
+184.0757 0.619071
+191.9647 0.590931
+219.0438 0.723589
+285.0221 3.694324
+287.0184 0.840167
+
+
+Name: Sulfachlorpyridazine
+Synon: 4-amino-N-(6-chloropyridazin-3-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100701
+InChIKey: XOXHILFPRYWFOD-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 285.0208
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: Ramp 21.1-31.6 eV
+Formula: C10H9ClN4O2S
+MW: 284
+ExactMass: 284.013474208
+Comments: "accession=AU100701" "author=Nikiforos Alygizakis, Anna Bletsou, Nikolaos Thomaidis, University of Athens" "license=CC BY" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=284.0135" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=Ramp 21.1-31.6 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.6 min" "solvent a=water with 0.01% formic acid and 5mM ammonium formate" "solvent b=90:10 methanol:water with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=285.0208" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.17469602228006656" "mass error=4.9792000027082395E-5" "SMILES=c1cc(ccc1N)S(=O)(=O)Nc2ccc(nn2)Cl" "cas=80-32-0" "pubchem cid=6634" "chemspider=6382" "InChI=InChI=1S/C10H9ClN4O2S/c11-9-5-6-10(14-13-9)15-18(16,17)8-3-1-7(12)2-4-8/h1-6H,12H2,(H,14,15)" "InChIKey=XOXHILFPRYWFOD-UHFFFAOYSA-N" "molecular formula=C10H9ClN4O2S" "total exact mass=284.013474208" "SMILES=C=1C=C(C=CC1N)S(NC=2C=CC(Cl)=NN2)(=O)=O"
+Num Peaks: 27
+53.0389 0.594951
+54.0333 0.566811
+55.0178 0.522592
+60.0552 0.542692
+65.0382 3.822962
+66.0423 0.506512
+68.049 7.963499
+78.0333 0.727609
+79.0177 1.057244
+92.0498 7.702203
+93.0532 0.731629
+96.0443 0.623091
+108.0457 12.172375
+109.0483 1.181862
+110.0609 4.904325
+120.0562 3.095353
+130.0172 5.656054
+132.0138 1.515517
+156.0118 100.000000
+157.015 8.884065
+158.008 3.891301
+174.0228 0.751729
+184.0757 0.619071
+191.9647 0.590931
+219.0438 0.723589
+285.0221 3.694324
+287.0184 0.840167
+
+
+Name: Sulfadimidine
+Synon: 4-amino-N-(4,6-dimethylpyrimidin-2-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100801
+InChIKey: ASWVTGNCAZCNNR-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 279.091
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: Ramp 20.8-31.3 eV
+Formula: C12H14N4O2S
+MW: 278
+ExactMass: 278.08374668799996
+Comments: "accession=AU100801" "author=Nikiforos Alygizakis, Anna Bletsou, Nikolaos Thomaidis, University of Athens" "license=CC BY" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=278.0837" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=Ramp 20.8-31.3 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.4 min" "solvent a=water with 0.01% formic acid and 5mM ammonium formate" "solvent b=90:10 methanol:water with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=279.091" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.08129248146496051" "mass error=-2.2687999944537296E-5" "SMILES=Cc1cc(nc(n1)NS(=O)(=O)c2ccc(cc2)N)C" "cas=57-68-1" "kegg=C19530" "pubchem cid=5327" "chemspider=5136" "InChI=InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=ASWVTGNCAZCNNR-UHFFFAOYSA-N" "molecular formula=C12H14N4O2S" "total exact mass=278.08374668799996" "SMILES=CC1=CC(C)=NC(=N1)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 46
+53.0379 0.894101
+54.0335 0.661867
+55.0176 0.598003
+65.0379 8.717487
+68.0491 13.013818
+69.0329 1.640153
+78.0334 1.477589
+79.0178 2.261379
+80.0489 1.431143
+81.0444 1.950766
+82.0284 0.606712
+92.0499 30.585230
+93.0558 2.844868
+94.0647 1.686600
+95.0608 3.027752
+96.0443 1.300511
+108.0461 33.946818
+109.0497 2.360079
+110.0616 6.107757
+111.0651 0.519624
+120.0565 1.962378
+122.0716 6.078727
+123.0794 2.246865
+124.0872 71.211681
+125.0905 6.398049
+126.0663 17.911054
+127.0697 0.595100
+156.0117 82.855318
+157.0148 5.739085
+158.0072 1.544357
+174.0224 1.106015
+186.0334 11.263353
+187.0368 0.775081
+188.0128 1.637250
+188.0291 0.534138
+204.0445 100.000000
+205.0473 6.972829
+206.0406 3.358686
+213.1141 18.259405
+214.1167 2.241059
+215.0927 3.071296
+215.1291 1.320831
+279.0925 61.483976
+280.0953 8.438806
+281.0725 7.837901
+282.0742 1.222132
+
+
+Name: Sulfamethazine
+Synon: 4-amino-N-(4,6-dimethylpyrimidin-2-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100802
+InChIKey: ASWVTGNCAZCNNR-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 279.091
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: 20 eV
+Formula: C12H14N4O2S
+MW: 278
+ExactMass: 278.08374668799996
+Comments: "accession=AU100802" "author=Nikiforos Alygizakis, Nikolaos Thomaidis, University of Athens" "license=CC BY-SA" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=278.0837467" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=20 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.1 min" "solvent a=90:10 water:methanol with 0.01% formic acid and 5mM ammonium formate" "solvent b=methanol with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=279.091" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.08129248146496051" "mass error=-2.2687999944537296E-5" "SMILES=CC1=CC(C)=NC(NS(=O)(=O)C2=CC=C(N)C=C2)=N1" "cas=57-68-1" "chebi=102265" "kegg=D02436" "pubchem cid=5327" "chemspider=5136" "InChI=InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=ASWVTGNCAZCNNR-UHFFFAOYSA-N" "molecular formula=C12H14N4O2S" "total exact mass=278.08374668799996" "SMILES=CC1=CC(C)=NC(=N1)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 16
+122.0703 0.766124
+124.0861 36.693459
+125.0892 1.930893
+149.0227 0.828453
+156.0104 53.249536
+157.0129 2.999571
+158.0061 1.778967
+174.0209 0.627183
+186.0321 22.621444
+187.0346 1.719235
+188.0285 0.646661
+204.0431 100.000000
+213.1128 8.749399
+214.1159 1.407591
+215.1281 0.658348
+279.0909 80.894937
+
+
+Name: Sulfamethazine
+Synon: 4-amino-N-(4,6-dimethylpyrimidin-2-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100803
+InChIKey: ASWVTGNCAZCNNR-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 279.091
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: 30 eV
+Formula: C12H14N4O2S
+MW: 278
+ExactMass: 278.083746688
+Comments: "accession=AU100803" "author=Nikiforos Alygizakis, Nikolaos Thomaidis, University of Athens" "license=CC BY-SA" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=278.0837467" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=30 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.2 min" "solvent a=90:10 water:methanol with 0.01% formic acid and 5mM ammonium formate" "solvent b=methanol with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=279.091" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.08129248166863394" "mass error=-2.2688000001380715E-5" "SMILES=CC1=CC(C)=NC(NS(=O)(=O)C2=CC=C(N)C=C2)=N1" "cas=57-68-1" "chebi=102265" "kegg=D02436" "pubchem cid=5327" "chemspider=5136" "InChI=InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=ASWVTGNCAZCNNR-UHFFFAOYSA-N" "molecular formula=C12H14N4O2S" "total exact mass=278.083746688" "SMILES=CC1=CC(C)=NC(=N1)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 17
+108.0441 1.285794
+122.0704 6.630847
+123.0781 2.170942
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+125.0889 6.093546
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+187.0355 1.323064
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+213.1123 22.610100
+214.1155 3.003292
+215.1283 0.804398
+279.0903 3.580968
+
+
+Name: Sulfamethazine
+Synon: 4-amino-N-(4,6-dimethylpyrimidin-2-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100804
+InChIKey: ASWVTGNCAZCNNR-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 279.091
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: 40 eV
+Formula: C12H14N4O2S
+MW: 278
+ExactMass: 278.083746688
+Comments: "accession=AU100804" "author=Nikiforos Alygizakis, Nikolaos Thomaidis, University of Athens" "license=CC BY-SA" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=278.0837467" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=40 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.1 min" "solvent a=90:10 water:methanol with 0.01% formic acid and 5mM ammonium formate" "solvent b=methanol with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=279.091" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.08129248166863394" "mass error=-2.2688000001380715E-5" "SMILES=CC1=CC(C)=NC(NS(=O)(=O)C2=CC=C(N)C=C2)=N1" "cas=57-68-1" "chebi=102265" "kegg=D02436" "pubchem cid=5327" "chemspider=5136" "InChI=InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=ASWVTGNCAZCNNR-UHFFFAOYSA-N" "molecular formula=C12H14N4O2S" "total exact mass=278.083746688" "SMILES=CC1=CC(C)=NC(=N1)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 22
+108.0445 1.153673
+122.0702 5.323878
+123.0772 2.202467
+124.0862 100.000000
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+205.0463 0.869001
+206.0375 0.759127
+212.1036 0.659242
+213.1121 18.109174
+214.1152 2.577036
+
+
+Name: Sulfamethazine
+Synon: 4-amino-N-(4,6-dimethylpyrimidin-2-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100805
+InChIKey: ASWVTGNCAZCNNR-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 279.091
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: 50 eV
+Formula: C12H14N4O2S
+MW: 278
+ExactMass: 278.083746688
+Comments: "accession=AU100805" "author=Nikiforos Alygizakis, Nikolaos Thomaidis, University of Athens" "license=CC BY-SA" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=278.0837467" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=50 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.2 min" "solvent a=90:10 water:methanol with 0.01% formic acid and 5mM ammonium formate" "solvent b=methanol with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=279.091" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.08129248166863394" "mass error=-2.2688000001380715E-5" "SMILES=CC1=CC(C)=NC(NS(=O)(=O)C2=CC=C(N)C=C2)=N1" "cas=57-68-1" "chebi=102265" "kegg=D02436" "pubchem cid=5327" "chemspider=5136" "InChI=InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=ASWVTGNCAZCNNR-UHFFFAOYSA-N" "molecular formula=C12H14N4O2S" "total exact mass=278.083746688" "SMILES=CC1=CC(C)=NC(=N1)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 24
+108.0453 1.770916
+122.0703 2.803951
+123.078 2.792598
+124.0859 100.000000
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+204.0438 2.622318
+212.1048 2.327165
+213.1122 9.342718
+214.1153 1.725508
+
+
+Name: Sulfamethazine
+Synon: 4-amino-N-(4,6-dimethylpyrimidin-2-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100806
+InChIKey: ASWVTGNCAZCNNR-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 279.091
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: 10 eV
+Formula: C12H14N4O2S
+MW: 278
+ExactMass: 278.08374668799996
+Comments: "accession=AU100806" "author=Nikiforos Alygizakis, Nikolaos Thomaidis, University of Athens" "license=CC BY-SA" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=278.0837467" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=10 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.2 min" "solvent a=90:10 water:methanol with 0.01% formic acid and 5mM ammonium formate" "solvent b=methanol with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=279.091" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.08129248146496051" "mass error=-2.2687999944537296E-5" "SMILES=CC1=CC(C)=NC(NS(=O)(=O)C2=CC=C(N)C=C2)=N1" "cas=57-68-1" "chebi=102265" "kegg=D02436" "pubchem cid=5327" "chemspider=5136" "InChI=InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=ASWVTGNCAZCNNR-UHFFFAOYSA-N" "molecular formula=C12H14N4O2S" "total exact mass=278.08374668799996" "SMILES=CC1=CC(C)=NC(=N1)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 4
+124.086 0.740586
+156.0098 1.123942
+186.0319 0.831793
+279.0908 100.000000
+
+
+Name: Sulfadimethoxine
+Synon: 4-amino-n-(2,6-dimethoxypyrimidin-4-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100902
+InChIKey: ZZORFUFYDOWNEF-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 311.0809
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: 20 eV
+Formula: C12H14N4O4S
+MW: 310
+ExactMass: 310.07357592799997
+Comments: "accession=AU100902" "author=Nikiforos Alygizakis, Anna Bletsou, Nikolaos Thomaidis, University of Athens" "license=CC BY-SA" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=310.0735759" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=20 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.6 min" "solvent a=water with 0.01% formic acid and 5mM ammonium formate" "solvent b=90:10 methanol:water with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=311.0809" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.15453214911901536" "mass error=4.8072000026877504E-5" "SMILES=COc1cc(nc(n1)OC)NS(=O)(=O)c2ccc(cc2)N" "cas=122-11-2" "chebi=32161" "pubchem=5323" "chemspider=5132" "InChI=InChI=1S/C12H14N4O4S/c1-19-11-7-10(14-12(15-11)20-2)16-21(17,18)9-5-3-8(13)4-6-9/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=ZZORFUFYDOWNEF-UHFFFAOYSA-N" "molecular formula=C12H14N4O4S" "total exact mass=310.07357592799997" "SMILES=COC=1C=C(N=C(N1)OC)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 15
+140.0447 6.249276
+141.0515 0.699085
+154.0604 5.781932
+155.0683 3.398864
+156.0107 100.000000
+156.0763 16.893901
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+157.0794 0.857441
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+245.1032 9.010853
+246.1061 0.834267
+311.0811 75.335059
+312.0835 7.145340
+313.0796 2.301958
+
+
+Name: Sulfadimethoxine
+Synon: 4-amino-n-(2,6-dimethoxypyrimidin-4-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100903
+InChIKey: ZZORFUFYDOWNEF-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 311.0809
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: 30 eV
+Formula: C12H14N4O4S
+MW: 310
+ExactMass: 310.073575928
+Comments: "accession=AU100903" "author=Nikiforos Alygizakis, Anna Bletsou, Nikolaos Thomaidis, University of Athens" "license=CC BY-SA" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=310.0735759" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=30 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.6 min" "solvent a=water with 0.01% formic acid and 5mM ammonium formate" "solvent b=90:10 methanol:water with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=311.0809" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.15453214893628664" "mass error=4.8071999970034085E-5" "SMILES=COc1cc(nc(n1)OC)NS(=O)(=O)c2ccc(cc2)N" "cas=122-11-2" "chebi=32161" "pubchem=5323" "chemspider=5132" "InChI=InChI=1S/C12H14N4O4S/c1-19-11-7-10(14-12(15-11)20-2)16-21(17,18)9-5-3-8(13)4-6-9/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=ZZORFUFYDOWNEF-UHFFFAOYSA-N" "molecular formula=C12H14N4O4S" "total exact mass=310.073575928" "SMILES=COC=1C=C(N=C(N1)OC)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 21
+108.0448 1.310092
+124.0204 1.354502
+126.0659 3.563895
+127.0504 0.843788
+138.0294 1.576552
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+154.0604 60.575108
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+156.0105 100.000000
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+245.1039 10.447430
+246.107 1.176862
+311.0829 3.819252
+
+
+Name: Sulfadimethoxine
+Synon: 4-amino-n-(2,6-dimethoxypyrimidin-4-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100904
+InChIKey: ZZORFUFYDOWNEF-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 311.0809
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: 40 eV
+Formula: C12H14N4O4S
+MW: 310
+ExactMass: 310.073575928
+Comments: "accession=AU100904" "author=Nikiforos Alygizakis, Anna Bletsou, Nikolaos Thomaidis, University of Athens" "license=CC BY-SA" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=310.0735759" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=40 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.7 min" "solvent a=water with 0.01% formic acid and 5mM ammonium formate" "solvent b=90:10 methanol:water with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=311.0809" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.15453214893628664" "mass error=4.8071999970034085E-5" "SMILES=COc1cc(nc(n1)OC)NS(=O)(=O)c2ccc(cc2)N" "cas=122-11-2" "chebi=32161" "pubchem=5323" "chemspider=5132" "InChI=InChI=1S/C12H14N4O4S/c1-19-11-7-10(14-12(15-11)20-2)16-21(17,18)9-5-3-8(13)4-6-9/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=ZZORFUFYDOWNEF-UHFFFAOYSA-N" "molecular formula=C12H14N4O4S" "total exact mass=310.073575928" "SMILES=COC=1C=C(N=C(N1)OC)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 27
+112.0515 2.118270
+123.0436 2.184466
+124.0205 1.897617
+124.0508 2.162401
+126.0666 5.803177
+127.0502 2.030009
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+141.0524 38.989409
+142.058 2.449250
+154.0604 100.000000
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+213.0728 2.581642
+229.0713 2.206531
+230.0797 6.421006
+231.0852 1.919682
+245.1026 1.919682
+
+
+Name: Sulfadimethoxine
+Synon: 4-amino-n-(2,6-dimethoxypyrimidin-4-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU100905
+InChIKey: ZZORFUFYDOWNEF-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 311.0809
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: 50 eV
+Formula: C12H14N4O4S
+MW: 310
+ExactMass: 310.07357592799997
+Comments: "accession=AU100905" "author=Nikiforos Alygizakis, Anna Bletsou, Nikolaos Thomaidis, University of Athens" "license=CC BY-SA" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=310.0735759" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=50 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.7 min" "solvent a=water with 0.01% formic acid and 5mM ammonium formate" "solvent b=90:10 methanol:water with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=311.0809" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.15453214911901536" "mass error=4.8072000026877504E-5" "SMILES=COc1cc(nc(n1)OC)NS(=O)(=O)c2ccc(cc2)N" "cas=122-11-2" "chebi=32161" "pubchem=5323" "chemspider=5132" "InChI=InChI=1S/C12H14N4O4S/c1-19-11-7-10(14-12(15-11)20-2)16-21(17,18)9-5-3-8(13)4-6-9/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=ZZORFUFYDOWNEF-UHFFFAOYSA-N" "molecular formula=C12H14N4O4S" "total exact mass=310.07357592799997" "SMILES=COC=1C=C(N=C(N1)OC)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 22
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+123.0427 7.773689
+124.0502 6.439742
+126.0287 5.841766
+126.0666 6.255750
+127.0491 3.955842
+132.0559 9.521619
+133.0628 5.105796
+138.0293 10.579577
+140.045 45.768169
+141.0521 46.780129
+142.0539 3.817847
+154.0606 100.000000
+156.0102 3.679853
+156.0405 5.243790
+156.0769 17.157314
+157.0629 5.887764
+160.0507 3.909844
+178.0613 9.429623
+184.0741 4.737810
+201.0768 9.015639
+212.0705 7.589696
+
+
+Name: Sulfadoxine
+Synon: 4-amino-N-(5,6-dimethoxypyrimidin-4-yl)benzenesulfonamide
+SYNON: $:00in-source
+DB#: AU101001
+InChIKey: PJSFRIWCGOHTNF-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 311.0809
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: Ramp 21.8-32.7 eV
+Formula: C12H14N4O4S
+MW: 310
+ExactMass: 310.07357592799997
+Comments: "accession=AU101001" "author=Nikiforos Alygizakis, Anna Bletsou, Nikolaos Thomaidis, University of Athens" "license=CC BY" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=310.0736" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=Ramp 21.8-32.7 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=4.8 min" "solvent a=water with 0.01% formic acid and 5mM ammonium formate" "solvent b=90:10 methanol:water with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=311.0809" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.15453214911901536" "mass error=4.8072000026877504E-5" "SMILES=COc1c(ncnc1OC)NS(=O)(=O)c2ccc(cc2)N" "cas=2447-57-6" "kegg=C07630" "pubchem cid=17134" "chemspider=16218" "InChI=InChI=1S/C12H14N4O4S/c1-19-10-11(14-7-15-12(10)20-2)16-21(17,18)9-5-3-8(13)4-6-9/h3-7H,13H2,1-2H3,(H,14,15,16)" "InChIKey=PJSFRIWCGOHTNF-UHFFFAOYSA-N" "molecular formula=C12H14N4O4S" "total exact mass=310.07357592799997" "SMILES=COC1=C(N=CN=C1OC)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 42
+53.0386 0.535490
+54.0339 0.505437
+65.0381 7.755041
+68.0491 10.088247
+69.0329 1.049123
+78.0332 1.038195
+79.0179 1.721217
+80.0363 0.707612
+80.0493 1.446642
+92.0498 46.272062
+93.0559 3.808535
+96.0447 1.331894
+108.0463 57.395771
+109.049 3.816731
+109.0643 0.531392
+110.0614 7.111633
+113.0359 0.703513
+120.0568 1.860554
+124.0215 1.529971
+124.0512 0.572373
+126.0665 2.939730
+138.0301 0.707612
+140.0457 34.351948
+141.0528 5.744222
+154.0615 32.562428
+155.0682 7.798754
+156.0118 100.000000
+156.0771 40.377575
+157.0147 7.961314
+157.0796 2.486203
+158.0078 3.766188
+201.0773 1.349653
+212.0697 2.576362
+213.0752 0.527294
+218.0236 1.945249
+230.0808 5.531119
+231.085 0.811431
+245.1045 18.128791
+246.1073 2.479373
+311.0829 49.986340
+312.0854 8.491339
+313.0812 2.222556
+
+
+Name: Sulfadiazine
+Synon: 4-amino-n-pyrimidin-2-ylbenzenesulfonamide
+SYNON: $:00in-source
+DB#: AU101101
+InChIKey: SEEPANYCNGTZFQ-UHFFFAOYSA-N
+Precursor_type: [M+H]+
+Spectrum_type: MS2
+PrecursorMZ: 251.0597
+Instrument_type: LC-ESI-QTOF
+Instrument: Bruker maXis Impact
+Ion_mode: P
+Collision_energy: 10 eV
+Formula: C10H10N4O2S
+MW: 250
+ExactMass: 250.05244656
+Comments: "accession=AU101101" "author=Nikiforos Alygizakis, Anna Bletsou, Nikolaos Thomaidis, University of Athens" "license=CC BY-SA" "copyright=Copyright (C) 2015 Department of Chemistry, University of Athens" "exact mass=250.0524466" "instrument=Bruker maXis Impact" "instrument type=LC-ESI-QTOF" "ms level=MS2" "ionization=ESI" "fragmentation mode=CID" "collision energy=10 eV" "resolution=35000" "column=Acclaim RSLC C18 2.2um, 2.1x100mm, Thermo" "flow gradient=99/1 at 0-1 min, 61/39 at 3 min, 0.1/99.9 at 14-16 min, 99/1 at 16.1-20 min" "flow rate=200 uL/min at 0-3 min, 400 uL/min at 14 min, 480 uL/min at 16-19 min, 200 uL/min at 19.1-20 min" "retention time=3.3 min" "solvent a=water with 0.01% formic acid and 5mM ammonium formate" "solvent b=90:10 methanol:water with 0.01% formic acid and 5mM ammonium formate" "precursor m/z=251.0597" "precursor type=[M+H]+" "ionization mode=positive" "mass accuracy=0.08985910518808851" "mass error=-2.2559999990789947E-5" "SMILES=c1cnc(nc1)NS(=O)(=O)c2ccc(cc2)N" "cas=141582-64-1" "chebi=9328" "kegg=C07658" "pubchem=5215" "chemspider=5026" "InChI=InChI=1S/C10H10N4O2S/c11-8-2-4-9(5-3-8)17(15,16)14-10-12-6-1-7-13-10/h1-7H,11H2,(H,12,13,14)" "InChIKey=SEEPANYCNGTZFQ-UHFFFAOYSA-N" "molecular formula=C10H10N4O2S" "total exact mass=250.05244656" "SMILES=C1=CN=C(N=C1)NS(C2=CC=C(C=C2)N)(=O)=O"
+Num Peaks: 6
+156.0106 9.361897
+174.0199 0.724251
+176.012 0.693756
+251.0596 100.000000
+252.0616 7.867653
+253.0565 2.729283
\ No newline at end of file
diff --git a/tests/test_databases.py b/tests/test_databases.py
index daae038..20c7ff7 100644
--- a/tests/test_databases.py
+++ b/tests/test_databases.py
@@ -25,6 +25,7 @@
 import shutil
 import pickle
 from metaboblend.databases import *
+from metaboblend.parse import reformat_xml
 
 
 class DatabasesTestCase(unittest.TestCase):
diff --git a/tests/test_isomorphism_database.py b/tests/test_isomorphism_database.py
index 96ff705..adba71e 100644
--- a/tests/test_isomorphism_database.py
+++ b/tests/test_isomorphism_database.py
@@ -21,6 +21,7 @@
 
 
 import os
+import sys
 import unittest
 import shutil
 import tempfile
diff --git a/tests/test_parse.py b/tests/test_parse.py
new file mode 100644
index 0000000..5fc95eb
--- /dev/null
+++ b/tests/test_parse.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright © 2019-2020 Ralf Weber
+#
+# This file is part of MetaboBlend.
+#
+# MetaboBlend is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# MetaboBlend is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with MetaboBlend.  If not, see <https://www.gnu.org/licenses/>.
+#
+
+
+import os
+import unittest
+import shutil
+import tempfile
+from metaboblend.parse import *
+
+
+class IsomorphDbTestCase(unittest.TestCase):
+    temp_results_dir = None
+
+    @classmethod
+    def to_test_results(cls, *args):
+        return os.path.join(os.path.dirname(os.path.realpath(__file__)), cls.temp_results_dir.name, *args)
+
+    @classmethod
+    def to_test_data(cls, *args):
+        return os.path.join(os.path.dirname(os.path.realpath(__file__)), cls.temp_results_dir.name, "test_data", *args)
+
+    @classmethod
+    def setUpClass(cls):
+        cls.temp_results_dir = tempfile.TemporaryDirectory(dir=os.path.dirname(os.path.realpath(__file__)))
+
+        shutil.copytree(os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data"),
+                        cls.to_test_results("test_data"))
+
+    def test_parse_msp(self):
+        for i, ms in enumerate(parse_msp(self.to_test_data("mona_msp.msp"))):
+
+            if i < 2:
+                self.assertEqual(ms, None)
+            else:
+                print(ms)
+
+        self.assertEqual(list(parse_msp(self.to_test_data("massbank_msp.txt")))[0], None)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_suite_auxiliary.py b/tests/test_suite_auxiliary.py
index c44171d..cc211e2 100644
--- a/tests/test_suite_auxiliary.py
+++ b/tests/test_suite_auxiliary.py
@@ -27,6 +27,7 @@
 from pathlib import Path
 
 from . import test_auxiliary
+from . import test_parse
 
 sys.path.insert(0, str(Path(__file__).parent.parent.resolve()))
 
@@ -35,6 +36,7 @@
     suite = unittest.TestSuite()
 
     suite.addTest(unittest.findTestCases(test_auxiliary))
+    suite.addTest(unittest.findTestCases(test_parse))
 
     report = os.path.join(os.path.abspath(os.path.join(__file__, os.pardir)), 'results', 'results_test_suite_auxiliary')
     runTestSuite(suite, report, title='Process Test Suite Report', verbosity=2)

From 5caf8f6cb9c7312054482d68555195fd962411f5 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Mon, 30 Nov 2020 14:18:07 +0000
Subject: [PATCH 31/35] Add unit testing and documentation for parse.py

---
 metaboblend/build_structures.py |   9 ++-
 metaboblend/parse.py            |  88 ++++++++++++++++++++---
 tests/test_parse.py             | 123 +++++++++++++++++++++++++++++++-
 3 files changed, 207 insertions(+), 13 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index 105a14a..d445e20 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -31,7 +31,6 @@
 
 from rdkit import Chem
 
-
 from .results import ResultsDb
 from .parse import parse_ms_data
 from .algorithms import subset_sum
@@ -155,6 +154,9 @@ def add_bonds(mols, edges, atoms_available, bond_types, bond_enthalpies):
 
             * **2.0** Double
 
+    :param bond_enthalpies: Dictionary of bond enthalpies, as generated by
+    :py:meth:`metaboblend.build_structures.get_bond_enthalpies`.
+
     :return: If unsuccessful, returns None, else returns an :py:meth:`rdkit.Chem.EditableMol` object containing
         the substructures combined into a final single molecule.
     """
@@ -789,6 +791,11 @@ def substructure_combination_build(substructure_subset, configs_iso, prescribed_
 
     :param isomeric_smiles: True/False, should output smiles be written with isomeric information?
 
+    :param bond_enthalpies: Dictionary of bond enthalpies, as generated by
+    :py:meth:`metaboblend.build_structures.get_bond_enthalpies`.
+
+    :param retain_substructures: Whether to record the substructures used to generate final structures.
+
     :return: List of smiles representing molecules generated (and the substructures used to generate them).
     """
 
diff --git a/metaboblend/parse.py b/metaboblend/parse.py
index 28aa2f2..5495a58 100644
--- a/metaboblend/parse.py
+++ b/metaboblend/parse.py
@@ -30,11 +30,17 @@
 
 def parse_ms_data(ms_data, msn=True):
     """
-    Parse raw data provided by user and yield formatted input data.
+    Parse raw data provided by user and yield formatted input data. Decides what type of data has been provided
+    (i.e. whether a dictionary has been given vs path to MSP file; if a dictionary, checks whether neutral masses
+    need to be calculated from precursor ions).
 
     :param ms_data: Dictionary containing input data or path to an MSP file.
 
-    :return: None
+    :param msn: If True, formats the data for use by :py:meth:`metaboblend.build_structures.annotate_msn`; else, formats
+        input data for use by :py:meth:`metaboblend.build_structures.generate_structures`. Only relevant if a
+        dictionary has been provided.
+
+    :return: Yields a dictionary for use by build functions to generate structures.
     """
 
     if isinstance(ms_data, dict):
@@ -65,16 +71,41 @@ def parse_ms_data(ms_data, msn=True):
 
 
 def precursor_ion_to_neutral_mass(mass, precursor_type):
-    """ Convert precursor ion to predicted neutral mass for substructure searching. """
+    """
+    Convert precursor ion to predicted neutral mass for substructure searching.
+
+    :param mass: Charged mass to be neutralised.
+
+    :param precursor_type: Type of precursor ion.
+
+    :return: Neutral mass.
+    """
 
     # conversions
-    precursor_dict = {"[M+H]+":  1.007276}
+    precursor_dict = {"[M+H]+":  1.007276,
+                      "[M+Na]+": 22.989221,
+                      "[M+K]+": 38.963158,
+                      "[M-H]-": -1.007276,
+                      "[M+Cl]-": 34.969401,
+                      "[M+Na-2H]-": 20.974668,
+                      "[M+K-2H]-": 36.948605,
+                      "[M+Hac-H]-": 59.013853}
 
     return mass - precursor_dict[precursor_type]
 
 
 def precursor_ions_to_neutral_masses(ms_dict, which="both"):
-    """ Convert precursor ion and fragment ions to neutral. """
+    """
+    Convert precursor ion and fragment ions to neutral.
+
+    :param ms_dict: Dictionary used by build functions to generate structures. Converts the precursor ion mass and/or
+        the fragment ions to their respective neutral masses.
+
+    :param which: Whether to convert the precursor ion ("precursor"), the fragment ions ("fragments") or both ("both")
+        to their respective neutral masses. If which is "none", returns the original dictionary.
+
+    :return: Returns `ms_dict` with additional items corresponding to neutralised masses.
+    """
 
     if which == "precursor" or which == "both":
         ms_dict["exact_mass"] = precursor_ion_to_neutral_mass(ms_dict["precursor_mz"],
@@ -92,12 +123,28 @@ def precursor_ions_to_neutral_masses(ms_dict, which="both"):
 
 
 def parse_msp(msp_path):
-    """ Parse msp files and yield data for each compound. """
+    """
+    Parse msp files and yield data for each compound. Accepts MSP files in MoNa or MassBank format. We expect that
+    the following are provided in the MSP:
+
+    - A unique accession ID.
+    - The molecular formula of the compound.
+    - The precursor mz representing the mass of the charged precursor ion.
+    - Fragment mzs representing masses of charged fragment ions.
+    - The type of precursor, e.g. "[M+H]+".
+
+    Code adapted from `msp2db` (https://github.com/computational-metabolomics/msp2db/blob/master/msp2db/parse.py).
+
+    :param msp_path: Path of an MSP file to be converted into a dictionary.
+
+    :return: Dictionary in a form useable by :py:meth:`metaboblend.build_structures.annotate_msn` and
+        :py:meth:`metaboblend.build_structures.generate_structures`.
+    """
 
     meta_parse = get_msp_regex()
     reached_spectra = False
 
-    empty_dict = {"ms_id": None, "mf": None, "precursor_mz": None, "fragment_mzs": []}
+    empty_dict = {"ms_id": None, "mf": None, "precursor_mz": None, "precursor_type": None, "fragment_mzs": []}
     entry_dict = copy.deepcopy(empty_dict)
 
     with open(msp_path, "r") as msp_file:
@@ -134,7 +181,24 @@ def parse_msp(msp_path):
 
 
 def reformat_msp_input(entry_dict):
-    """ Reformat input for use by build functions. """
+    """
+    Reformat input for use by build functions.
+
+    :param entry_dict: Dictionary containing MSn information extracted from an MSP file (by
+        :py:meth:`metaboblend.parse.parse_msp`. The dictionary must contain the following:
+
+        - ms_id - a unique accession number
+        - mf - the molecular formula of the compound (in the format "CXHXNXOXPXSX")
+        - precursor_mz - mz representing the mass of the charged precursor ion
+        - precursor_type - the type of precursor ion (e.g. "[M+H]+")
+        - fragment_mzs - mz(s) representing the mass of charged fragment ions
+
+    :return: If the correct inputs were not provided in the MSP (and, hence, were not available in `entry_dict`),
+        returns None (and generates a warning with i) the accession (if available) and ii) the variable that was not
+        able to be extracted from the MSP). Else, returns the same dictionary after reformatting the molecular formula,
+        using :py:meth:`metaboblend.parse.mc_to_list`, and converting the precursor ions to their corresponding
+        neutral masses.
+    """
 
     if entry_dict["mf"] is not None:  # convert from C5H6... to [5, 6, ...]
         entry_dict["mf"] = mc_to_list(entry_dict["mf"])
@@ -157,7 +221,13 @@ def reformat_msp_input(entry_dict):
 
 
 def mc_to_list(mc):
-    """ Convert molecular formula to list format. """
+    """
+    Convert molecular formula string to list format.
+
+    :param mc: Molecular formula (in the format "C1H2N3O4P5S6")
+
+    :return: Molecular formula (in the format `[1, 2, 3, 4, 5, 6]`)
+    """
 
     if isinstance(mc, list):
         return mc
diff --git a/tests/test_parse.py b/tests/test_parse.py
index 5fc95eb..513fb68 100644
--- a/tests/test_parse.py
+++ b/tests/test_parse.py
@@ -21,9 +21,10 @@
 
 
 import os
-import unittest
+import copy
 import shutil
 import tempfile
+import unittest
 from metaboblend.parse import *
 
 
@@ -44,6 +45,13 @@ def setUpClass(cls):
 
         shutil.copytree(os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data"),
                         cls.to_test_results("test_data"))
+        
+        cls.neutral_fragment_masses = [155.00332400000002, 173.01262400000002, 175.004724,
+                                       250.052324, 251.054324, 252.049224]
+        cls.exact_mass = 250.052424
+        cls.mf = [10, 10, 4, 2, 0, 1]
+        cls.precursor_mz = 251.0597
+        cls.fragment_mzs = [156.0106, 174.0199, 176.012, 251.0596, 252.0616, 253.0565]
 
     def test_parse_msp(self):
         for i, ms in enumerate(parse_msp(self.to_test_data("mona_msp.msp"))):
@@ -51,10 +59,119 @@ def test_parse_msp(self):
             if i < 2:
                 self.assertEqual(ms, None)
             else:
-                print(ms)
+                self.assertNotEqual(ms, None)
+
+        self.assertEqual(ms, {"ms_id": "AU101101", "mf": self.mf, "precursor_mz": self.precursor_mz,
+                              "fragment_mzs": self.fragment_mzs, "precursor_type": "[M+H]+",
+                              "exact_mass": self.exact_mass, "neutral_fragment_masses": self.neutral_fragment_masses})
 
         self.assertEqual(list(parse_msp(self.to_test_data("massbank_msp.txt")))[0], None)
 
+        # ensure that parse_msp provides same output as parse_ms_data when providing an msn file
+        for parse_msp_dict, parse_ms_dict in zip(parse_msp(self.to_test_data("mona_msp.msp")),
+                                                 parse_ms_data(self.to_test_data("mona_msp.msp"))):
+
+            self.assertEqual(parse_msp_dict, parse_ms_dict)
+
+    def test_parse_ms_data(self):
+
+        # exact mass and neutral fragment masses should not be overwritten by parse_ms_data
+        full_ms_dict = {"ms_id": "AU101101", "mf": self.mf, "precursor_mz": self.precursor_mz,
+                        "fragment_mzs": self.fragment_mzs, "precursor_type": "[M+H]+", "exact_mass": "abcd",
+                        "neutral_fragment_masses": ["a", "b", "c", "d"]}
+
+        self.assertEqual(list(parse_ms_data({"AU101101": copy.deepcopy(full_ms_dict)}))[0], full_ms_dict)
+
+        # if exact mass is present should not be overwritten by parse_ms_data
+        exact_mass_ms_dict = {"ms_id": "AU101101", "mf": self.mf, "precursor_mz": self.precursor_mz,
+                              "fragment_mzs": self.fragment_mzs, "precursor_type": "[M+H]+", "exact_mass": "abc"}
+
+        parsed_exact_mass_ms_dict = list(parse_ms_data({"test": copy.deepcopy(exact_mass_ms_dict)}))[0]
+        exact_mass_ms_dict["ms_id"] = "test"
+        exact_mass_ms_dict["neutral_fragment_masses"] = self.neutral_fragment_masses
+        self.assertEqual(parsed_exact_mass_ms_dict, exact_mass_ms_dict)
+
+        # neutral fragment masses should not be overwritten by parse_ms_data
+        neutral_fragment_masses_ms_dict = {"ms_id": "AU101101", "mf": self.mf, "precursor_mz": self.precursor_mz,
+                                           "precursor_type": "[M+H]+", "fragment_mzs": self.fragment_mzs,
+                                           "neutral_fragment_masses": ["a", "b", "c", "d"]}
+
+        parsed_neutral_fragment_masses_ms_dict = list(parse_ms_data({"AU101101": copy.deepcopy(neutral_fragment_masses_ms_dict)}))[0]
+        neutral_fragment_masses_ms_dict["exact_mass"] = self.exact_mass
+        self.assertEqual(parsed_neutral_fragment_masses_ms_dict, neutral_fragment_masses_ms_dict)
+
+        uncalculated_ms_dict = {"ms_id": "AU101101", "mf": self.mf, "precursor_mz": self.precursor_mz,
+                                "fragment_mzs": self.fragment_mzs, "precursor_type": "[M+H]+"}
+        parsed_uncalculated_ms_dict = list(parse_ms_data({"AU101101": copy.deepcopy(uncalculated_ms_dict)}))[0]
+        uncalculated_ms_dict["exact_mass"] = self.exact_mass
+        uncalculated_ms_dict["neutral_fragment_masses"] = self.neutral_fragment_masses
+        self.assertEqual(parsed_uncalculated_ms_dict, uncalculated_ms_dict)
+
+        # test with msn=False
+        generate_structures_dict = {"ms_id": "AU101101", "mf": self.mf, "precursor_mz": self.precursor_mz, 
+                                    "prescribed_mass": "m", "precursor_type": "[M+H]+"}
+        parsed_generate_structures_dict = list(parse_ms_data({"AU101101": copy.deepcopy(generate_structures_dict)}, False))[0]
+        generate_structures_dict["exact_mass"] = self.exact_mass
+        self.assertEqual(parsed_generate_structures_dict, generate_structures_dict)
+
+        # test with exact mass provided
+        generate_structures_dict["exact_mass"] = "a"
+        parsed_generate_structures_dict = list(parse_ms_data({"AU101101": copy.deepcopy(generate_structures_dict)}, False))[0]
+        self.assertEqual(parsed_generate_structures_dict, generate_structures_dict)
+
+    def test_precursor_ions_to_neutral_masses(self):
+
+        ms_dict = {"ms_id": "AU101101", "mf": self.mf, "precursor_mz": self.precursor_mz,
+                        "fragment_mzs": self.fragment_mzs, "precursor_type": "[M+H]+"}
+
+        for which in ["both", "fragments", "precursor", "none"]:
+            processed_ms_dict = precursor_ions_to_neutral_masses(copy.deepcopy(ms_dict), which)
+
+            if which in ["both", "fragments"]:
+                self.assertEqual(processed_ms_dict["neutral_fragment_masses"], self.neutral_fragment_masses)
+
+            if which in ["both", "precursor"]:
+                self.assertEqual(processed_ms_dict["exact_mass"], self.exact_mass)
+
+        ms_dict["precursor_type"] = "[M-H]-"
+
+        for which in ["both", "fragments", "precursor", "none"]:
+            processed_ms_dict = precursor_ions_to_neutral_masses(copy.deepcopy(ms_dict), which)
+
+            if which in ["both", "fragments"]:
+                neutral_fragment_masses = [nfm + 1.007276 for nfm in self.fragment_mzs]
+                self.assertEqual(processed_ms_dict["neutral_fragment_masses"], neutral_fragment_masses)
+
+            if which in ["both", "precursor"]:
+                self.assertEqual(processed_ms_dict["exact_mass"], self.precursor_mz + 1.007276)
+
+    def test_reformat_msp_input(self):
+
+        unformatted_msp_dict = {'ms_id': 'AU101101', 'mf': 'C10H10N4O2S', 'precursor_mz': '251.0597',
+                                'fragment_mzs': self.fragment_mzs,
+                                'precursor_type': '[M+H]+'}
+
+        formatted_msp_dict = {'ms_id': 'AU101101', 'mf': self.mf, 'precursor_mz': self.precursor_mz,
+                                'fragment_mzs': self.fragment_mzs, 'precursor_type': '[M+H]+',
+                                'exact_mass': self.exact_mass,
+                                'neutral_fragment_masses': self.neutral_fragment_masses}
+
+        self.assertEqual(reformat_msp_input(unformatted_msp_dict), formatted_msp_dict)
+
+        unformatted_msp_dict["precursor_mz"] = None
+        self.assertWarns(UserWarning, reformat_msp_input(unformatted_msp_dict))
+
+        unformatted_msp_dict["precursor_mz"] = self.precursor_mz
+        unformatted_msp_dict["fragment_mzs"] = []
+        self.assertWarns(UserWarning, reformat_msp_input(unformatted_msp_dict))
+
+    def test_mc_to_list(self):
+
+        mc_lists = [[12, 14, 4, 4, 0, 1], [10, 10, 4, 2, 0, 1], [46, 94, 1, 8, 1, 0], [46, 94, 1, 8, 1, 0], None]
+
+        for i, word_formula in enumerate(["C12H14N4O4S", "C10H10N4O2S", "C46H94NO8P", "C46H94NO8P1", "C10H9ClN4O2S"]):
+            self.assertEqual(mc_to_list(word_formula), mc_lists[i])
+
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()

From bdd71e46b0bc344e68dd886ab7be291f9443922d Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Mon, 30 Nov 2020 14:47:55 +0000
Subject: [PATCH 32/35] Amend connectivity database unit tests so that they
 fail in case of the generation of an empty DB

---
 tests/test_isomorphism_database.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/tests/test_isomorphism_database.py b/tests/test_isomorphism_database.py
index adba71e..6bda34a 100644
--- a/tests/test_isomorphism_database.py
+++ b/tests/test_isomorphism_database.py
@@ -67,6 +67,7 @@ def setUpClass(cls):
                                     [1, 2],  # boxes
                                     cls.path_ri
                                     )
+        shutil.copyfile(cls.to_test_results("connectivity.sqlite"), "connectivity.sqlite")
 
     def test_create_connectivity_database(self):
         ref_db = sqlite3.connect(self.to_test_data("connectivity.sqlite"))
@@ -77,12 +78,12 @@ def test_create_connectivity_database(self):
         test_db_cursor = test_db.cursor()
         test_db_cursor.execute("SELECT * FROM subgraphs")
 
-        ref_rows = {}
-        for row in ref_db_cursor.fetchall():
-            ref_rows[row[0]] = row
-
+        test_rows = {}
         for row in test_db_cursor.fetchall():
-            self.assertEqual(row, ref_rows[row[0]])
+            test_rows[row[0]] = row
+
+        for row in ref_db_cursor.fetchall():
+            self.assertEqual(row, test_rows[row[0]])
 
         ref_db.close()
         test_db.close()

From 40c516aa1c644be49e5ff0f080f47d77429fd308 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Mon, 30 Nov 2020 14:51:51 +0000
Subject: [PATCH 33/35] Amend results docstrings

---
 metaboblend/results.py             | 10 ++++------
 tests/test_isomorphism_database.py |  1 -
 2 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/metaboblend/results.py b/metaboblend/results.py
index 573ca26..898443f 100644
--- a/metaboblend/results.py
+++ b/metaboblend/results.py
@@ -45,13 +45,13 @@ def __init__(self, path_results, msn=True):
         self.substructure_combo_id = 0
 
     def connect(self):
-        """Connects to the results database."""
+        """ Connects to the results database. """
 
         self.conn = sqlite3.connect(self.path_results_db)
         self.cursor = self.conn.cursor()
 
     def create_results_db(self):
-        """Generates a new results database."""
+        """ Generates a new results database. """
 
         if os.path.exists(self.path_results_db):
             os.remove(self.path_results_db)
@@ -269,9 +269,7 @@ def get_structures(self, ms_id_num):
             return [item for t in self.cursor.fetchall() for item in t]
 
     def generate_csv_output(self):
-        """
-        Generate CSV file output for i) queries and tool parameters and ii) structures generated.
-        """
+        """ Generate CSV file output for i) queries and tool parameters and ii) structures generated. """
 
         with open(os.path.join(self.path_results, "metaboblend_queries.csv"), "w", newline="") as results_file, \
                 open(os.path.join(self.path_results, "metaboblend_structures.csv"), "w", newline="") as ms_file:
@@ -296,6 +294,6 @@ def generate_csv_output(self):
                 ms_writer.writerow(structure)
 
     def close(self):
-        """Close the connection to the SQLITE3 database."""
+        """ Close the connection to the SQLITE3 database. """
 
         self.conn.close()
diff --git a/tests/test_isomorphism_database.py b/tests/test_isomorphism_database.py
index 6bda34a..0304114 100644
--- a/tests/test_isomorphism_database.py
+++ b/tests/test_isomorphism_database.py
@@ -67,7 +67,6 @@ def setUpClass(cls):
                                     [1, 2],  # boxes
                                     cls.path_ri
                                     )
-        shutil.copyfile(cls.to_test_results("connectivity.sqlite"), "connectivity.sqlite")
 
     def test_create_connectivity_database(self):
         ref_db = sqlite3.connect(self.to_test_data("connectivity.sqlite"))

From 04260a7f2ca8f455b6ce1308bd7ae38b08b9e4e5 Mon Sep 17 00:00:00 2001
From: Jack Gisby <43494691+jackgisby@users.noreply.github.com>
Date: Mon, 30 Nov 2020 16:53:10 +0000
Subject: [PATCH 34/35] Update docstrings of user-facing build functions

---
 metaboblend/build_structures.py | 33 +++++++++++++++++++++++----------
 1 file changed, 23 insertions(+), 10 deletions(-)

diff --git a/metaboblend/build_structures.py b/metaboblend/build_structures.py
index d445e20..16987dc 100644
--- a/metaboblend/build_structures.py
+++ b/metaboblend/build_structures.py
@@ -234,11 +234,18 @@ def annotate_msn(msn_data: Union[str, os.PathLike, Dict[str, Dict[str, Union[int
     text format. For the generation of structures without MSn data, see
     :py:meth:`metaboblend.build_structures.generate_structures`.
 
-    :param msn_data: Dictionary in the form
-        `msn_data[id] = {mf: [C, H, N, O, P, S], exact_mass: float, fragment_masses=[]}`. id represents a unique
-        identifier for a given spectral tree or fragmentation spectrum, mf is a list of integers referring to the
-        molecular formula of the structure of interest, exact_mass is the mass of this molecular formula to >=4d.p.
-        and fragment_masses are neutral fragment masses generated by this structure used to inform candidate scoring.
+    :param msn_data: Either a dictionary or the path to an MSP file. MSP files are parsed by
+        :py:meth:`metaboblend.parse.parse_ms_data` before being converted into a dictionary. If a dictionary is
+        provided, it must contain one item per fragmentation spectrum; the keys of the dictionary should be a unique ID
+        for the query and the corresponding value must itself be a dictionary, containing the following:
+
+        - "exact_mass": `float` (neutral mass of query) OR "precursor_mz": `float` (mz of precursor ion)
+        - "mf": `[C, H, N, O, P, S]` (a list of 6 integers)
+        - "neutral_fragment_masses": `[float, float, ...]` (list of neutral fragment masses) OR "fragment_mzs":
+            `[float, float, ...]` (list of fragment mzs)
+        - "precursor_type": `str` (e.g. "[M+H]+", required for calculating neutral masses from ion mzs)
+
+        The dictionary or MSP path is fed to :py:meth:`metaboverse.parse.parse_ms_data`.
 
     :param path_substructure_db: The path to the SQLite 3 substructure database, as generated by
         :py:meth:`metaboblend.databases.SubstructureDb`.
@@ -383,11 +390,17 @@ def generate_structures(ms_data: Union[str, os.PathLike, Dict[str, Dict[str, Uni
     text format. For the generation of structures from MSn data, see
     :py:meth:`metaboblend.build_structures.annotate_msn`.
 
-    :param ms_data: Dictionary in the form ms_data[id] =
-        `{mf: [C, H, N, O, P, S], exact_mass: float, prescribed_mass=int}`. id represents a unique identifier for
-        a given test, mf is a list of integers referring to molecular formula of the structure of interest,
-        exact_mass is the mass of this structure to >=4d.p. and prescribed_mass is the neutral mass of a substructure
-        used to limit structures generated.
+    :param ms_data: A dictionary that must contain one item per fragmentation spectrum; the keys of the dictionary
+        should be a unique ID for the query and the corresponding value must itself be a dictionary, containing the
+        following:
+
+        - "exact_mass": `float` (neutral mass of query) OR "precursor_mz": `float` (mz of precursor ion)
+        - "mf": `[C, H, N, O, P, S]` (a list of 6 integers)
+        - "precursor_type": `str` (e.g. "[M+H]+", required for calculating neutral masses from ion mzs)
+        - (optional) "prescribed_mass": 'float' (neutral mass of substructure).
+
+        The dictionary or MSP path is fed to :py:meth:`metaboverse.parse.parse_ms_data`. A single neutral substructure
+        mass may be provided ("prescribed_mass") to guide the structure generation process.
 
     :param path_substructure_db: The path to the SQLite 3 substructure database, as generated by
         :py:meth:`metaboblend.databases.SubstructureDb`.

From 399b9a8553a670d55ffddcff649a8bccb8a09e49 Mon Sep 17 00:00:00 2001
From: Jack Gisby <jackgisby@gmail.com>
Date: Thu, 3 Dec 2020 09:58:29 +0000
Subject: [PATCH 35/35] Only test isomorphism database on non-windows systems

---
 tests/test_isomorphism_database.py | 62 ++++++++++++++++--------------
 1 file changed, 33 insertions(+), 29 deletions(-)

diff --git a/tests/test_isomorphism_database.py b/tests/test_isomorphism_database.py
index 0304114..6ad11a4 100644
--- a/tests/test_isomorphism_database.py
+++ b/tests/test_isomorphism_database.py
@@ -46,46 +46,50 @@ def setUpClass(cls):
         shutil.copytree(os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data"),
                         cls.to_test_results("test_data"))
 
+    def test_create_connectivity_database(self):
+
         pkg_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+
         if sys.platform == "win32" or sys.platform == "win64":  # TODO: add RI as dependency
-            cls.path_ri = os.path.join(pkg_path, "tools", "RI_win", "RI3.6-release", "ri36")
+            self.path_ri = os.path.join(pkg_path, "tools", "RI_win", "RI3.6-release", "ri36")
 
-        elif sys.platform == "darwin":
-            cls.path_ri = os.path.join(pkg_path, "tools", "RI_mac", "RI3.6-release", "ri36")
+        else:
 
-        elif sys.platform == "linux2":
-            if "bb" in "socket.gethostname":
-                cls.path_ri = os.path.join(pkg_path, "tools", "RI_unix", "RI3.6-release", "ri36")
-            else:
-                cls.path_ri = os.path.join(pkg_path, "tools", "RI_bb", "RI3.6-release", "ri36")
+            if sys.platform == "darwin":
+                self.path_ri = os.path.join(pkg_path, "tools", "RI_mac", "RI3.6-release", "ri36")
 
-        elif sys.platform == "linux":
-            cls.path_ri = os.path.join(pkg_path, "tools", "RI_unix", "RI3.6-release", "ri36")
+            elif sys.platform == "linux2":
+                if "bb" in "socket.gethostname":
+                    self.path_ri = os.path.join(pkg_path, "tools", "RI_unix", "RI3.6-release", "ri36")
+                else:
+                    self.path_ri = os.path.join(pkg_path, "tools", "RI_bb", "RI3.6-release", "ri36")
 
-        create_connectivity_database(cls.to_test_results("connectivity.sqlite"),
-                                    3,  # sizes
-                                    [1, 2],  # boxes
-                                    cls.path_ri
-                                    )
+            elif sys.platform == "linux":
+                self.path_ri = os.path.join(pkg_path, "tools", "RI_unix", "RI3.6-release", "ri36")
 
-    def test_create_connectivity_database(self):
-        ref_db = sqlite3.connect(self.to_test_data("connectivity.sqlite"))
-        ref_db_cursor = ref_db.cursor()
-        ref_db_cursor.execute("SELECT * FROM subgraphs")
+            create_connectivity_database(self.to_test_results("connectivity.sqlite"),
+                                         3,  # sizes
+                                         [1, 2],  # boxes
+                                         self.path_ri
+                                         )
+
+            ref_db = sqlite3.connect(self.to_test_data("connectivity.sqlite"))
+            ref_db_cursor = ref_db.cursor()
+            ref_db_cursor.execute("SELECT * FROM subgraphs")
 
-        test_db = sqlite3.connect(self.to_test_results("connectivity.sqlite"))
-        test_db_cursor = test_db.cursor()
-        test_db_cursor.execute("SELECT * FROM subgraphs")
+            test_db = sqlite3.connect(self.to_test_results("connectivity.sqlite"))
+            test_db_cursor = test_db.cursor()
+            test_db_cursor.execute("SELECT * FROM subgraphs")
 
-        test_rows = {}
-        for row in test_db_cursor.fetchall():
-            test_rows[row[0]] = row
+            test_rows = {}
+            for row in test_db_cursor.fetchall():
+                test_rows[row[0]] = row
 
-        for row in ref_db_cursor.fetchall():
-            self.assertEqual(row, test_rows[row[0]])
+            for row in ref_db_cursor.fetchall():
+                self.assertEqual(row, test_rows[row[0]])
 
-        ref_db.close()
-        test_db.close()
+            ref_db.close()
+            test_db.close()
 
 
 if __name__ == '__main__':