gentle smiles parsing for lookup

chebifier/prediction_models/c3p_predictor.py

@@ -1,6 +1,8 @@
 from pathlib import Path
 from typing import List, Optional
 
+import tqdm
+
 from chebifier import modelwise_smiles_lru_cache
 from chebifier.prediction_models import BasePredictor
 
@@ -26,14 +28,22 @@ def __init__(
     def predict_smiles_list(self, smiles_list: list[str]) -> list:
         from c3p import classifier as c3p_classifier
 
-        result_list = c3p_classifier.classify(
-            list(smiles_list),
-            self.program_directory,
-            self.chemical_classes,
-            strict=False,
-        )
+        result_list = []
+        for batch_start in tqdm.tqdm(
+            range(0, len(smiles_list), 32), desc="Classifying with C3P"
+        ):
+            batch_end = min(batch_start + 32, len(smiles_list))
+            result_list.extend(
+                c3p_classifier.classify(
+                    smiles_list[batch_start:batch_end],
+                    self.program_directory,
+                    self.chemical_classes,
+                    strict=False,
+                )
+            )
+
         result_reformatted = [dict() for _ in range(len(smiles_list))]
-        for result in result_list:
+        for result in tqdm.tqdm(result_list, desc="Reformatting C3P results"):
             chebi_id = result.class_id.split(":")[1]
             result_reformatted[smiles_list.index(result.input_smiles)][
                 chebi_id
@@ -61,13 +71,13 @@ def explain_smiles(self, smiles):
                 highlights.append(
                     (
                         "text",
-                        f"For class {result.class_name} ({result.class_id}), C3P gave the following explanation: {result.reason}",
+                        f"For {result.class_name} ({result.class_id}), C3P gave the following explanation: {result.reason}",
                     )
                 )
         highlights = [
             (
                 "text",
-                f"C3P made positive predictions for {len(highlights)} classes. The explanations are as follows:",
+                f"C3P made positive predictions for {len(highlights)} classes. {'The explanations are as follows:' if len(highlights) > 0 else ''}",
             )
         ] + highlights
 

chebifier/prediction_models/chebi_lookup.py

@@ -6,7 +6,7 @@
 
 from chebifier import modelwise_smiles_lru_cache
 from chebifier.prediction_models import BasePredictor
-from chebifier.utils import load_chebi_graph
+from chebifier.utils import _smiles_to_mol, load_chebi_graph
 
 
 class ChEBILookupPredictor(BasePredictor):
@@ -50,7 +50,7 @@ def build_smiles_lookup(self):
         ).items():
             if smiles is not None:
                 try:
-                    mol = Chem.MolFromSmiles(smiles)
+                    mol = _smiles_to_mol(smiles)
                     if mol is None:
                         print(
                             f"Failed to parse SMILES {smiles} for ChEBI ID {chebi_id}"
@@ -72,7 +72,7 @@ def build_smiles_lookup(self):
     def predict_smiles(self, smiles: str) -> Optional[dict]:
         if not smiles:
             return None
-        mol = Chem.MolFromSmiles(smiles)
+        mol = _smiles_to_mol(smiles)
         if mol is None:
             return None
         canonical_smiles = Chem.MolToSmiles(mol)
@@ -110,7 +110,7 @@ def info_text(self):
         return self._description
 
     def explain_smiles(self, smiles: str) -> dict:
-        mol = Chem.MolFromSmiles(smiles)
+        mol = _smiles_to_mol(smiles)
         if mol is None:
             return {
                 "highlights": [

chebifier/utils.py

@@ -1,3 +1,4 @@
+import functools
 import importlib.resources
 import os
 import pickle
@@ -6,6 +7,7 @@
 import networkx as nx
 import requests
 import yaml
+from rdkit import Chem
 
 from chebifier.hugging_face import download_model_files
 
@@ -156,6 +158,18 @@ def process_config(config, model_registry):
     return new_config
 
 
+@functools.lru_cache(maxsize=128)
+def _smiles_to_mol(smiles: str):
+    mol = Chem.MolFromSmiles(smiles, sanitize=False)
+    if mol is not None:
+        # turn aromatic bond types into single/double
+        try:
+            Chem.Kekulize(mol)
+        except Chem.KekulizeException as e:
+            print(f"Failed to Kekulize {smiles}: {e}")
+    return mol
+
+
 if __name__ == "__main__":
     chebi_graph = build_chebi_graph(chebi_version=244)
     os.makedirs(os.path.join("data", "chebi_v244"), exist_ok=True)