GS-Meta

This repository is the official implementation of GS-Meta proposed in: Graph Sampling-based Meta-Learning for Molecular Property Prediction, IJCAI 2023.

Environment

To run the code successfully, the following dependencies need to be installed:

python           3.7
torch            1.7.1
rdkit            2022.9.3
learn2learn      0.1.6
torch_geometric  1.6.3
torch_scatter    2.0.7

Step-by-step guidelines

Datasets

For data used in the experiments, please download data.zip from the release page in this repo, then extract the downloaded file and save the contents in the data directory.

Project Overview

This project mainly contains the following parts.

├── data                              # dataset files                 
│   ├── sider                       
│   │   ├── sider.csv    
│   ├── tox21                 
│   │   ├── tox21.csv  
│   └── ...
├── datasets                        
│   ├── __init__.py
│   └── ...
├── models                        
│   ├── __init__.py
│   └── ...
├── pretraiend                        # pretrained GNN
│   ├── supervised_contextpred.pth
├── args_parser.py                      
├── explight.py                      
├── meta_learner.py                    
└── run.py                        

Running Script

 python run.py --dataset sider --n_support 10 --gpu 0 

Running parameters and descriptions are as follows:

Parameter	Description	Default Value	Choices
dataset	name of dataset	sider	tox21, sider, muv, pcba, toxcast-APR, toxcast-ATG, toxcast-BSK, toxcast-CEETOX, toxcast-CLD, toxcast-NVS, toxcast-OT, toxcast-Tanguay, toxcast-TOX21
n_support	number of support molecules	10	1, 10
gpu	which GPU to use	0	\
exp_name	experiment name	None	\
exp_id	experiment ID	None	\
eval_step	evaluation interval	100	\

For Pre-GS-Meta, which is initialized with a pretrained GNN, the running script is:

 python run.py --dataset sider --n_support 10 --gpu 0 --mol_pretrain_load_path pretrained/supervised_contextpred.pth

References

If you use or extend our work, please cite the paper as follows:

@InProceedings{zhuang2023graph,
  title={Graph Sampling-based Meta-Learning for Molecular Property Prediction},
  author={Xiang Zhuang and Qiang Zhang and Bin Wu and Keyan Ding and Yin Fang and Huajun Chen},
  booktile={IJCAI},
  year={2023}
}