Learning Topology-Specific Experts for Molecular Property Prediction
This is Official Pytorch Implementation for the paper “Learning Topology-Specific Experts for Molecular Property Prediction”. Suyeon Kim, Dongha Lee, SeongKu Kang, Seonghyeon Lee, Hwanjo Yu (AAAI-23)
The paper is available at Link.
Run
python main.py --dataset bbbp
We refer the baseline code to build our implementation.
https://github.com/snap-stanford/pretrain-gnns
Package Install
conda create -n topexpert python=3.8
conda activate topexpert
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
conda install -c conda-forge rdkit
conda install pytorch-geometric -c rusty1s -c conda-forge
Cite (Bibtex)
- If you find
TopExpert
useful in your research, please consider citing:
@article{kim2023learning,
title={Learning Topology-Specific Experts for Molecular Property Prediction},
author={Suyeon Kim, Dongha Lee, SeongKu Kang, Seonghyeon Lee, Hwanjo Yu},
booktitle={AAAI},
year={2023}
}
Learning Topology-Specific Experts for Molecular Property Prediction
This is Official Pytorch Implementation for the paper “Learning Topology-Specific Experts for Molecular Property Prediction”. Suyeon Kim, Dongha Lee, SeongKu Kang, Seonghyeon Lee, Hwanjo Yu (AAAI-23)
The paper is available at Link.
Run
We refer the baseline code to build our implementation. https://github.com/snap-stanford/pretrain-gnns
Package Install
Cite (Bibtex)
TopExpert
useful in your research, please consider citing: