ClassicalGSG: Prediction of logP Using Classical Molecular Dynamics Atomic Attributes and Geometric Scattering Graphs

This project is the implementation of a method called ClassicalGSG and has introduced in ClassicalGSG: Prediction of logP using classical molecular force fields and geometric scattering for graphs. In this project, we aim to predict the partition coefficient value for the small molecules.

Here, we use molecular features generated using a recently developed method called Geometric Scattering for Graphs (GSG). The GSG method uses the graph structure of molecules to transform atomic attributes into index-variant molecular features.

The atomic attributes are generated using two classical molecular dynamics force fields generator tool CGenFF and Ambertools. We train ClassicalGSG logP predictor models with neural networks (NNs) which, are implemented using PyTorch.

Installation

You should use conda to make a new virtual environment:

conda create -n myenv python=3.7
conda activate myenv

Currently you must manually install some of the dependencies using conda. Do this first:

conda install -c pytorch pytorch
conda install -c conda-forge openbabel rdkit

To install from pip:

pip install classicalgsg

You can install from the git repo as well:

pip install git+https://github.com/ADicksonLab/ClassicalGSG.git

Usage

To use our CGenFF ClassicalGSG logP predictor model run the following command:

python -m LogpPredictor_CGenFF [molecule.mol2] [molecule.str]

To use our MMFF94 ClassicalGSG logP predictor model run the following command:

python -m LogpPredictor_MMFF94  ['smiles']

You can generate CGenFF parameter files for your molecule using CGenFF online server.

Dataset

The logP dataset can be downloaded from Zenodo. Zenodo DOI: 10.5281/zenodo.4531015