README.md

Instructions

Environments

Install packages under conda env

conda create -n GraphMVP python=3.7
conda activate GraphMVP

conda install -y -c rdkit rdkit
conda install -y -c pytorch pytorch=1.9.1
conda install -y numpy networkx scikit-learn
pip install ase
pip install git+https://github.com/bp-kelley/descriptastorus
pip install ogb
export TORCH=1.9.0
export CUDA=cu102  # cu102, cu110

wget https://data.pyg.org/whl/torch-${TORCH}%2B${CUDA}/torch_cluster-1.5.9-cp37-cp37m-linux_x86_64.whl
pip install torch_cluster-1.5.9-cp37-cp37m-linux_x86_64.whl
wget https://data.pyg.org/whl/torch-${TORCH}%2B${CUDA}/torch_scatter-2.0.9-cp37-cp37m-linux_x86_64.whl
pip install torch_scatter-2.0.9-cp37-cp37m-linux_x86_64.whl
wget https://data.pyg.org/whl/torch-${TORCH}%2B${CUDA}/torch_sparse-0.6.12-cp37-cp37m-linux_x86_64.whl
pip install torch_sparse-0.6.12-cp37-cp37m-linux_x86_64.whl
pip install torch-geometric==1.7.2

Or just use

conda env create -f fragcl.yaml

Dataset Preprocessing

First of all, place your SMILES file at datasets/smiles.csv

For data preprocessing, please use the following commands:

cd src_classification
python GEOM_dataset_preparation_frag_allsingle.py --data_folder ../datasets
cd ..

Experiments

For pretraining, please use the followng commands:

cd scripts_classification
bash submit_pretraining_fragcl.sh

Then, the model will be saved at output folder.