This repository contains a variety of tools that will be useful in automating the process of chemical perception for the new SMIRKS Native Open Force Field (SMIRNOFF) format as a part of the Open Force Field Initiative .
ChemPer can be used to automatically generate SMIRKS patterns to match clustered molecular fragments.
For example, you may have calculated bond lengths and force constants for a variety of bonds in one group of molecules.
You could use that data to cluster those bonds and then use
ChemPer to generate SMIRKS patterns which would allow
you to apply those lengths and force constants to a new set of molecules.
The algorithms implemented here were inspired by
SMARTY and SMIRKY which were proven to be too inefficient for
practical use in force field parameterization .
For a more extensive history and explanation, see our preprint .
We test with Python 3.6 and 3.7 and expect any version above 3.5 to behave well.
This is a python tool kit with a few dependencies. We recommend installing miniconda. Then you can create an environment with the following commands:
conda create -n [my env name] python=3.6 numpy networkx pytest source activate [my env name]
This command will install all dependencies besides a toolkit for cheminformatics or storing of molecule information. We seek to keep this tool independent of cheminformatics toolkit, but currently only support RDKit and OpenEye Toolkits. If you wish to add support please feel free to submit a pull request. Make sure one of these tool kits is installed in your environment before installing chemper.
Conda installation according to RDKit documentation:
conda install -c rdkit rdkit
Conda installation according to OpenEye documentation
conda install -c openeye openeye-toolkits
Hopefully chemper will be conda installable in the near future, but for now the best option
is to download or clone this repository and then install
chemper from inside the
chemper directory with:
pip install -e .
chempers main function.
It takes groups of molecular fragments which should be typed together and generates a heirarchical list
of SMIRKS patterns which maintains this typing.
SMIRKSifier takes a list of molecules and groups of atoms based on index and generates
a hierarchical list of SMIRKS in just a few lines of code.
In the example, general_smirks_for_clusters
we cluster bonds in a set of simple hydrocarbons based on order. Then
SMIRKSifer turns these clusters into a list of SMIRKS patterns.
The following functionalities are used to make the
SMIRKSifier possible, but may be useful on their own.
The goal of this tool is to store all information about the atoms and bonds that could be in a SMIRKS pattern. These are created assuming you already have a clustered set of molecular subgraphs. As our primary goal is to determine chemical perception for force field parameterization we image the input data being clustered subgraphs based on what parameter we wish to assign those atoms, such as equilibrium bond lengths and force constants. However, you could imagine other reasons for wanting to store how you clustered groups of atoms.
For more detailed examples and illustration of how this works see SMIRKS_from_molecules. Below is a brief example showing the SMIRKS for the bond between two carbon atoms in propane and pentane.
from chemper.mol_toolkits import mol_toolkit from chemper.graphs.cluster_graph import ClusterGraph mol1 = mol_toolkit.Mol.from_smiles('CCC') mol2 = mol_toolkit.Mol.from_smiles('CCCCC') smirks_atom_lists = [[(0,1)], [(0,1), (1,2)]] graph = ClusterGraph([mol1, mol2], smirks_atom_lists) print(graph.as_smirks()) # '[#6AH2X4x0r0+0,#6AH3X4x0r0+0:1]-;!@[#6AH2X4x0r0+0:2]'
The idea with ClusterGraph objects is that they store all possible decorator information for each atom.
In this case the SMIRKS indexed atoms for propane (mol1) are one of the terminal and the middle carbons.
In pentane (mol2) however atom1 can be a terminal or middle of the chain carbon atom. This changes the number of
hydrogen atoms (
Hn decorator) on the carbon, thus there are two possible SMIRKS patterns for atom
#6AH2X4x0r0+0 or (indicated by the "
#6AH3X4x0r0+0. But, atom
:2 only has one possibility
The goal of this tool was to create an example of how you could create a SMIRKS pattern from a molecule and set of atom indices. While this isn't ultimately useful in sampling chemical perception as they only work for a single molecule, however it is a tool that did not exist to the best of the authors knowledge before. For a detailed example see the single_mol_smirks jupyter notebook.
Here is a brief usage example for creating the SMIRKS pattern for the bond between the two carbon
atoms in ethene including atoms one bond away from the indexed atoms. The indexed atoms are the two carbon
atoms at indices 0 and 1 in the molecule are assigned to SMIRKS indices
from chemper.mol_toolkits import mol_toolkit from chemper.graphs.single_graph import SingleGraph mol = mol_toolkit.Mol.from_smiles('C=C') # note this adds explicit hydrogens to your molecule smirks_atoms = (0,1) graph = SingleGraph(mol, smirks_atoms, layers=1) print(graph.as_smirks()) # [#6AH2X3x0r0+0:1](-!@[#1AH0X1x0r0+0])(-!@[#1AH0X1x0r0+0])=!@[#6AH2X3x0r0+0:2](-!@[#1AH0X1x0r0+0])-!@[#1AH0X1x0r0+0]
As noted above, we seek to keep
chemper independent of the underlying cheminformatics toolkits.
mol_toolkits was created to keep all code dependent on the toolkit isolated. It can create molecules from
an RDK or OE molecule object or from a SMILES string. It includes a variety of functions for extracting information
about atoms, bonds, and molecules. Also included here are subsearchs using indexed SMARTS (or SMIRKS) patterns.
0.1.0 Alpha Release
This is a first release of the Alpha testing version of
chemper. As you can follow in the issue tracker there are still
on going problems to resolve. This first release will allow for reference to the concepts and algorithms included here
for automated chemical perception. However, the API is still in flux and nothing should be considered permanent at this time.
This release matches the work published in our preprint. While the code is stable and there are tests showing how it should work the science it represents is still in the early stages and big changes to the algorithms and API should be expected in future releases.
CCB is funded by a fellowship from The Molecular Sciences Software Institute under NSF grant ACI-1547580.