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MoleOOD

License License Slides

Official implementation for our paper:

Learning Substructure Invariance for Out-of-Distribution Molecular Representations

Nianzu Yang, Kaipeng Zeng, Qitian Wu, Xiaosong Jia, Junchi Yan* (* denotes correspondence)

Advances in Neural Information Processing Systems (NeurIPS 2022)

Folder Specification

  • config/: configurations for backbone (GCN, GIN, GraphSAGE)
  • saved_model/: three trained model on OGB-BACE datasets
  • modules/: preprocessing scripts for data and model definition
  • baseline_ogb.py: train the baselines on OGB benchmark
  • main.py: train or evaluate our model on OGB benchmark

Package Dependency

torch: 1.9.0
numpy: 1.21.2
ogb: 1.3.4
rdkit: 2021.9.4
scikit-learn: 1.0.2
pyg: 2.0.3

Run the Code

Train the baselines on OGB benchmark:

python baseline_ogb.py --dataset ogbg-molbace --gnn gcn --device ${device} --seed ${seed}

Before training our model, we should obtain the substructures from the raw data (here we use BRICS molecular segmentation method):

python modules/PreProcess.py --dataset ogbg-molbace --method ${decomposition_method}

Then, we can train our model, e.g.:

python main.py --base_backend ./config/GCN_base_dp0.1.json --sub_backend ./config/GIN_sub_dp0.1.json --domain_backend ./config/GIN_domain_dp0.1.json --conditional_backend ./config/GIN_cond_dp0.1.json  --dataset ogbg-molbace --lambda_loss ${lambda_loss} --device ${device} --lr ${lr} --num_domain ${num_domain} --epoch_main ${epoch_main} --epoch_ast ${epoch_ast} --batch_size ${batch_size} --drop_ratio ${drop_ratio} --seed ${seed} --decomp_method ${decomposition_method} --prior ${uniform/gaussian}

or evaluate our model, e.g.:

python main.py --base_backend ./config/GCN_base_dp0.1.json --sub_backend ./config/GIN_sub_dp0.1.json --domain_backend ./config/GIN_domain_dp0.1.json --conditional_backend ./config/GIN_cond_dp0.1.json  --dataset ogbg-molbace --lambda_loss ${lambda_loss} --device ${device} --lr ${lr} --num_domain ${num_domain} --epoch_main ${epoch_main} --epoch_ast ${epoch_ast} --batch_size ${batch_size} --drop_ratio ${drop_ratio} --seed ${seed} --test --model_path ./saved_model/GCN.pth --decomp_method ${decomposition_method} --prior ${uniform/gaussian}

Dataset

We use four datasets from OGB benchmark and six datasets from DrugOOD benchmark.

OGB: BACE, BBBP, SIDER, HIV

DrugOOD: IC50/EC50-size/scaffold/assay

Citation

@inproceedings{yang2022learning,
  title={Learning Substructure Invariance for Out-of-Distribution Molecular Representations},
  author={Nianzu Yang and Kaipeng Zeng and Qitian Wu and Xiaosong Jia and Junchi Yan},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2022},
}

The codes for DrugOOD benchmark will be released soon.

Welcome to contact us [email protected] or [email protected] for any question.