Physics-informed generative model for drug-like molecule conformers

This package provides an implementation of the generative model as described by the article:

• Williams, D.C. and Inala, N. "Physics-informed generative model for drug-like molecule conformers." https://arxiv.org/abs/2403.07925, 10.1021/acs.jcim.3c01816

Requirements

This repository was tested with Python 3.10 and the following nonstandard packages:

Package	Release
PyTorch	1.12.1   1.13.0   2.0.0   2.0.1
RDKit	2021.09.2   2022.09.5
sqlite3	2.6.0
NetworkX	2.4
NumPy	1.23.4
Pandas	1.3.5
tqdm	4.57.0

Generation

Single molecule

The generate_one.py script can be used to generate multiple conformers for a single molecule represented as an isomeric SMILES string. For example:

> ./generate_one.py "C([C@@H]([C@@H]1C(=C(C(=O)O1)O)O)O)O"

This should produce the output file output/output.sdf with contents that look something like the following:

For best results, use isomeric SMILES, which provide explicit tetra chirality and cis/trans isomerism.

By default, the generator uses the model trained on QMugs, which is provided by this repository in the checkpoints directory. The model trained on GEOM-drugs is also available. Or you can train your own model (see instructions below).

By default, ten conformers are generated, using the CPU. Alternatives are available using command line options. Depending on hardware, employing a GPU could be faster, especially for a larger number of conformers or a particularly large molecule.

By default, only the final conformers are saved. The -trace option is provided if you wish to inspect intermediate solutions created during generation. Provide this option with a filename prefix, and the script will save a molecule file for each generated conformer that includes each step by appending an integer to the prefix. For example:

> ./generate_one.py "C([C@@H]([C@@H]1C(=C(C(=O)O1)O)O)O)O" --trace output/frame

produces ten files output/frame000.sdf, output/frame001.sdf, etc. The following is an animation created from an example of one of these files.

If you want to use guided generation using the simple repulsive term as described in the article, apply the --repulsion option. This can be useful for some difficult examples, such as natamycin, which contains a macrocycle.

> ./generate_one.py "C[C@@H]1C/C=C/C=C/C=C/C=C/[C@@H](C[C@H]2[C@@H]([C@H](C[C@](O2)(C[C@H](C[C@@H]3[C@H](O3)/C=C/C(=O)O1)O)O)O)C(=O)O)O[C@H]4[C@H]([C@H]([C@@H]([C@H](O4)C)O)N)O" --repulsion 0.5

Many molecules

The generate_many.py script can be used to generate multiple conformers for many molecules. The input should be a space-separated file containing one molecule per line with the first column the isomeric smiles and the second column a unique molecule name. The name is used to generate the output sdf files in the given directory.

> ./generate_many.py example.smi 
100%|████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 921.89it/s]
> ls output
aspirin.sdf  viagra.sdf  vitamin_C.sdf

By default, ten conformers are generated for each molecule, using the CUDA library. Batching is used to generate molecules in parallel. Use the --batch option to increase or decrease the number of batched atoms to match the memory limitations of your hardware.

Training

Data preparation

The provided training code expects the input conformers to be in a standard format, stored in a sqlite3 database. The script build_source.py can be used to build a database. For QMugs and/or GEOM-drugs, download the source data, edit the script appropriately (to point to where you have stored the files), and then invoke it. Creating the database is a one-time step that will take a few hours, depending on the speed of your system.

Data set	Download link
QMugs	https://libdrive.ethz.ch/index.php/s/X5vOBNSITAG5vzM
GEOM-drugs	https://dataverse.harvard.edu/api/access/datafile/4327252

The script is fairly simple and can be modified for other conformer sources.

Training

Training is performed using the train.py script.

> ./train.py data/qmugs.db

Depending on resources and the size of the dataset, training can take between a few hours or several days. A GPU or TPU is recommended. Adjust the batch size to match your memory resources. Performance may be limited by the speed of the file system containing the sqlite3 database. If you are in a hurry, using a portion of the dataset or fewer epochs will produce a useable model for experimentation, although accuracy may suffer to a degree.

Probing

Probing the structure of a model requires an example molecule and some careful accounting. An example is available in probe.ipynb.