/HM-GNN

Official PyTorch implementation of "Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks"

Primary LanguagePython

Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks

This is an official PyTorch implementation of the experiments in the following paper:
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks (ICML 2022)
Zhaoning Yu, Hongyang Gao

Requirements

pytorch                       1.9.0
rdkit-pypi                    2021.9.2
ogb                           1.3.1
dgl                           0.6.1
networkx

Part 1: Heterogeneous Motif Graph Construction

Run python preprocess.py to construct HM-graph for TUDataset.
Change the parameter of drop_node() function in the ops.py to drop noises in the motif dictionary.
Run python preprocess_hiv.py and python preprocess_pcba.py to construct HM-graph for ogbg-molhiv and ogbg-pcba dataset.
For ogbg-pcba dataset, because there are 11 graphs do not have motifs, you need to substract 11 from self.num_cliques.

Part 2: Training and evaluation

Run python main.py for TUDataset.
Run python main_ogbg_molhiv.py for ogbg-molhiv.
Run python main_molpcba.py for ogbg-pcba.

Cite

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

@inproceedings{yu2022molecular,
  title={Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks},
  author={Yu, Zhaoning and Gao, Hongyang},
  booktitle={International Conference on Machine Learning},
  pages={25581--25594},
  year={2022},
  organization={PMLR}
}