Motif-based Graph Self-Supervised Learning for Molecular Property Prediction

Official Pytorch implementation of NeurIPS'21 paper "Motif-based Graph Self-Supervised Learning for Molecular Property Prediction" (https://arxiv.org/abs/2110.00987).

Requirements

pytorch                   1.8.1             
torch-geometric           1.7.0
rdkit                     2020.09.1
tqdm                      4.31.1
tensorboardx              1.6

To install RDKit, please follow the instructions here http://www.rdkit.org/docs/Install.html

motif_based_pretrain/ contains codes for motif-based graph self-supervised pretraining.
finetune/ contains codes for finetuning on MoleculeNet benchmarks for evaluation.

Dataset

For the MoleculeNet dataset for finetuning, we have uploaded them to data.

Training

You can pretrain the model by

cd motif_based_pretrain
python pretrain_motif.py

Evaluation

You can evaluate the pretrained model by finetuning on downstream tasks

cd finetune
python finetune.py

Cite

If you find this repo to be useful, please cite our paper. Thank you.

@article{zhang2021motif,
  title={Motif-based Graph Self-Supervised Learning for Molecular Property Prediction},
  author={Zhang, Zaixi and Liu, Qi and Wang, Hao and Lu, Chengqiang and Lee, Chee-Kong},
  journal={arXiv preprint arXiv:2110.00987},
  year={2021}
}