/graph_level_drug_discovery

Primary LanguagePythonApache License 2.0Apache-2.0

Learning Graph-Level Representation for Drug Discovery

Paper Link: Learning Graph-Level Representation for Drug Discovery

Requirements

Usage

1.Clone the repository

git clone https://github.com/ZJULearning/graph_level_drug_discovery.git

2.Training

python train.py --gpu 0 --dataset pcba

Our train.py only supports 6 datasets in MoleculeNet, including Tox21, ToxCast, HIV, MUV, PCBA, SAMPL.

Result

Database and baseline: MoleculeNet

Dataset Split Method Train Valid Test
Tox21 Index 0.965 0.839 0.848
Tox21 Random 0.964 0.842 0.854
Tox21 Scaffold 0.971 0.788 0.759
ToxCast Index 0.927 0.747 0.734
ToxCast Random 0.924 0.746 0.768
ToxCast Scaffold 0.929 0.696 0.657
PCBA Index 0.904 0.869 0.864
PCBA Random 0.899 0.863 0.867
PCBA Scaffold 0.907 0.847 0.845

Citation

Please cite our work in your publications if it helps your research:

@article{Li2017Learning,
  Title={Learning Graph-Level Representation for Drug Discoveryk},
  Journal={arXiv preprint arXiv:1709.03741},
  Author={Junying Li, Deng Cai, Xiaofei He},
  Year={2017},
}