This is the official implementation of the DrugOOD project, this is the project page: https://drugood.github.io/
You can install the conda environment using the drugood.yaml file provided:
!git clone https://github.com/tencent-ailab/DrugOOD.git
!cd DrugOOD
!conda env create --name drugood --file=drugood.yaml
!conda activate drugoodThen you can go to the demo at demo/demo.ipynb which gives a quick practice on how to use DrugOOD.
For a quick practice on using DrugOOD for dataset curation and OOD benchmarking, one can refer to the demo/demo.ipynb.
First, you need to generate the required DrugOOD dataset with our code. The dataset curator currently focusing on generating datasets from CHEMBL. It supports the following two tasks:
- Ligand Based Affinity Prediction (LBAP).
- Structure Based Affinity Prediction (SBAP).
For OOD domain annotations, it supports the following 5 choices.
- Assay.
- Scaffold.
- Size.
- Protein. (only for SBAP task)
- Protein Family. (only for SBAP task)
For noise annotations, it supports the following three noise levels. Datasets with different noises are implemented by filters with different levels of strictness.
- Core.
- Refined.
- General.
At the same time, due to the inconvenient conversion between different measurement type (E.g. IC50, EC50, Ki, Potency), one needs to specify the measurement type when generating the dataset.
Firstly, specifiy the path of CHEMBL database and the directory to save the data in the configuration
file: configs/_base_/curators/lbap_defaults.py for LBAP task or configs/_base_/curators/sbap_defaults.py for SBAP task.
The source_root="YOUR_PATH/chembl_29_sqlite/chembl_29.db" means the path to the
chembl29 sqllite file. The target_root="data/" specifies the folder to save the generated data.
Note that you can download the original chembl29 database with sqllite format from http://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_29/chembl_29_sqlite.tar.gz.
The built-in configuration files are located in:
configs/curators/. Here we provide the 96 config files to reproduce the 96 datasets in our paper. Meanwhile,
you can also customize your own datasets by changing the config files.
Run tools/curate.py to generate dataset. Here are some examples:
Generate datasets for the LBAP task, with assay as domain, core as noise
level, IC50 as measurement type, LBAP as task type.:
python tools/curate.py --cfg configs/curators/lbap_core_ic50_assay.pyGenerate datasets for the SBAP task, with protein as domain, refined as noise level, EC50 as
measurement type, SBAP as task type.:
python tools/curate.py --cfg configs/curator/sbap_refined_ec50_protein.pyCurrently we support 6 different baseline algorithms:
- ERM
- IRM
- GroupDro
- Coral
- MixUp
- DANN
Meanwhile, we support various GNN backbones:
- GIN
- GCN
- Weave
- ShcNet
- GAT
- MGCN
- NF
- ATi-FPGNN
- GTransformer
And different backbones for protein sequence modeling:
- Bert
- ProteinBert
Firstly, run the following command to install.
python setup.py developRun the LBAP task with ERM algorithm:
python tools/train.py configs/algorithms/erm/lbap_core_ec50_assay_erm.pyIf you would like to run ERM on other datasets, change the corresponding options inside the above
config file. For example, ann_file = 'data/lbap_core_ec50_assay.json' specifies the input data.
Similarly, run the SBAP task with ERM algorithm:
python tools/train.py configs/algorithms/erm/sbap_core_ec50_assay_erm.py😄If you find this repo is useful, please consider to cite our paper:
@ARTICLE{2022arXiv220109637J,
author = {{Ji}, Yuanfeng and {Zhang}, Lu and {Wu}, Jiaxiang and {Wu}, Bingzhe and {Huang}, Long-Kai and {Xu}, Tingyang and {Rong}, Yu and {Li}, Lanqing and {Ren}, Jie and {Xue}, Ding and {Lai}, Houtim and {Xu}, Shaoyong and {Feng}, Jing and {Liu}, Wei and {Luo}, Ping and {Zhou}, Shuigeng and {Huang}, Junzhou and {Zhao}, Peilin and {Bian}, Yatao},
title = "{DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations}",
journal = {arXiv e-prints},
keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Quantitative Methods},
year = 2022,
month = jan,
eid = {arXiv:2201.09637},
pages = {arXiv:2201.09637},
archivePrefix = {arXiv},
eprint = {2201.09637},
primaryClass = {cs.LG}
}
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