HiMol

Hierarchical Molecular Graph Self-supervised Learning for Property Prediction

Official Pytorch implementation of HiMol model in the paper "Zang, Xuan., Zhao, Xianbing. & Tang, Buzhou. Hierarchical Molecular Graph Self-Supervised Learning for property prediction. Commun Chem 6, 34 (2023)." https://doi.org/10.1038/s42004-023-00825-5.

Environment Setup

python rdkit scipy torch torch-geometric torch-sparse tqdm networkx numpy pandas

Training

You can pretrain the model by

mkdir saved_model
python pretrain.py

Evaluation

You can evaluate the pretrained model by finetuning on downstream tasks

Download the downstream data from https://github.com/deepchem/deepchem/tree/master/deepchem/molnet/load_function, and save the .csv files in the ./finetune/dataset/[dataset_name]/raw/, where [dataset_name] is replaced by the downstream dataset name. For example, bace.csv is saved in './finetune/dataset/bace/raw/bace.csv'.

cd finetune
mkdir model_checkpoints
python finetune.py

Please cite our paper as follows. Thank you. " @article{zang2023hierarchical, title={Hierarchical Molecular Graph Self-Supervised Learning for property prediction}, author={Zang, Xuan and Zhao, Xianbing and Tang, Buzhou}, journal={Communications Chemistry}, volume={6}, number={1}, pages={34}, year={2023} } "