MoleOOD

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, Spotlight)

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

Codes for OGB Dataset

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 as default):

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

The preprocess results are already uploaded to the folder OGB/preprocess/.

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 to train main model} --epoch_ast ${epoch to train env inference model} --batch_size ${batch_size} --drop_ratio ${drop_ratio} --seed ${seed} --decomp_method ${decomposition_method} --prior ${uniform/gaussian}

or evaluate our model using following commands:

BACE+GCN:

python evaluate.py --base_backend ./config/GCN_base_dp0.1.json --sub_backend ./config/GIN_sub_dp0.1.json   --dataset ogbg-molbace  --model_path ./saved_model/GCN.pth --decomp_method brics --drop_ratio 0.1 --device ${device} 

BACE+GIN:

python evaluate.py --base_backend ./config/GIN_base_dp0.1.json --sub_backend ./config/GIN_sub_dp0.1.json   --dataset ogbg-molbace  --model_path ./saved_model/GIN.pth --decomp_method brics --drop_ratio 0.1 --device ${device} 

BACE+SAGE:

python evaluate.py --base_backend ./config/SAGE_base_dp0.1.json --sub_backend ./config/GIN_sub_dp0.1.json   --dataset ogbg-molbace  --model_path ./saved_model/SAGE.pth --decomp_method brics --drop_ratio 0.1 --device ${device} 

Codes for DrugOOD Dataset

Folder Specification

• data/: containing the data for training, including the preprocess result and substructure information merging scripts.
• config/: configuration for model and model training.
• main.py: the script to train our algorithm.
• models/: containing the loss definition, backbone definition for our method.
• saved_modesl/: six trained model on DrugOOD datasets

Package Dependency

torch: 1.11
pyg: 2.0.3
drugood: 0.0.1
rdkit: 2022.3.1
numpy: 1.12.2

To install package drugood, please refer to DrugOOD repository.

Data Generation

• The first step is to generate the original dataset from CHEMBL database. As for the detailed process or operation, please refer to the DrugOOD repository. The generated json files should be put into folder DrugOOD/data/ic50 or DrugOOD/data/ec50 respectively.

• The second step is to generate the substructure information for each molecule and merge it into original dataset. two operations should be run in this step as follows:

  python PreProcess.py --start ${start_index} --num ${num of molecule to process} --dataset ${ec50/ic50} --method ${decomposition method} --timeout ${maximum time to process a single molecule}
  

  After all the substructures are generated, change the working directory into DrugOOD/data/ic50 or DrugOOD/data/ec50 and run the script

  python merge_data.py
  

• All the processed results are uploaded to the folder DrugOOD/data/.

Run the Code

To train and evaluate the baseline on DrugOOD dataset, please refer to DrugOOD repository.

Our model can be trained like:

python main.py --data_config configs/data_assay_ec50.py --model_config configs/GIN_0.5_mean.py --lambda_loss ${lambda loss} --lr ${lr} --num_domain ${num domain} --seed ${seed} --epoch_ast ${epoch to train env inference model} --epoch_main ${epoch to train main model} --dist ${gaussian/uniform} --device ${device}

Also the well-trained models can be evaluated by:

ic50 assay:

python evaluate.py --data_config configs/data_assay_ic50.py --model_config configs/GIN_0.5_mean.py --model_path saved_models/ic50_assay.pth --device ${device}

ic50 scaffold:

python evaluate.py --data_config configs/data_scaffold_ic50.py --model_config configs/GIN_0.5_mean.py --model_path saved_models/ic50_scaffold.pth --device ${device}

ic50 size:

python evaluate.py --data_config configs/data_size_ic50.py --model_config configs/GIN_0.5_mean.py --model_path saved_models/ic50_size.pth --device ${device}

ec50 assay:

python evaluate.py --data_config configs/data_assay_ec50.py --model_config configs/GIN_0.5_mean.py --model_path saved_models/ec50_assay.pth --device ${device}

ec50 scaffold:

python evaluate.py --data_config configs/data_scaffold_ec50.py --model_config configs/GIN_0.5_mean.py --model_path saved_models/ec50_scaffold.pth --device ${device}

ec50 size:

python evaluate.py --data_config configs/data_size_ec50.py --model_config configs/GIN_0.5_mean.py --model_path saved_models/ec50_size.pth --device ${device}

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},
}

Welcome to contact us yangnianzu@sjtu.edu.cn or zengkaipeng@sjtu.edu.cn for any question.