Chirality-aware message passing networks

Custom aggregation functions for molecules with tetrahedral chirality (arXiv)

Requirements

• python (version>=3.7)
• pytorch (version>=1.14)
• rdkit (version>=2020.03.2)
• pytorch-geometric (version>=1.6.0)

Installation

First, clone the repository: git clone https://github.com/PattanaikL/chiral_gnn

Run make conda_env to create the conda environment. The script will request the user to enter one of the supported CUDA versions listed here: https://pytorch.org/get-started/locally/. The script uses this CUDA version to install PyTorch and PyTorch Geometric. Alternatively, the user could manually follow the steps to install PyTorch Geometric here: https://github.com/rusty1s/pytorch_geometric/blob/master/.travis.yml.

Usage

For the toy classification task, call the train.py script with the following parameters defined:

python train.py --data_path data/d4_docking/d4_docking_rs.csv --split_path data/d4_docking/rs/split0.npy --task classification --log_dir ./test_run --gnn_type dmpnn --message tetra_permute_concat

To train the model with the best-performing parameters, call the train.py script with the following parameters defined:

python train.py --data_path data/d4_docking/d4_docking.csv --split_path data/d4_docking/full/split0.npy --log_dir ./test_run --gnn_type dmpnn --message tetra_permute_concat --global_chiral_features --chiral_features