Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/tencent-ailab/DrugOOD

OOD Dataset Curator and Benchmark for AI-aided Drug Discovery
https://github.com/tencent-ailab/DrugOOD

Last synced: about 1 month ago
JSON representation

OOD Dataset Curator and Benchmark for AI-aided Drug Discovery

Awesome Lists containing this project

README 

        
# :fire:DrugOOD:fire::  OOD Dataset Curator and Benchmark for AI Aided Drug Discovery



This is the official implementation of the DrugOOD project, this is the project page:   



## Environment Installation



You can install the conda environment using the drugood.yaml file provided: 



```shell

!git clone https://github.com/tencent-ailab/DrugOOD.git

!cd DrugOOD

!conda env create --name drugood --file=drugood.yaml

!conda activate drugood

```   

Then you can go to the demo at `demo/demo.ipynb` which gives a quick practice on how to use DrugOOD.



## Demo



For a quick practice on using DrugOOD for dataset curation and OOD benchmarking, one can refer to the `demo/demo.ipynb`.   



## Dataset Curator



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.



### How to Run and Reproduce the 96 Datasets?



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.:



```shell

python tools/curate.py --cfg configs/curators/lbap_core_ic50_assay.py

```



Generate datasets for the SBAP task, with `protein` as domain, `refined` as noise level, `EC50` as

measurement type, `SBAP` as task type.:



```shell

python tools/curate.py --cfg configs/curator/sbap_refined_ec50_protein.py

```



## Benchmarking SOTA OOD Algorithms



Currently 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



### How to Run?



Firstly, run the following command to install.



```shell

python setup.py develop

```



Run the LBAP task with ERM algorithm:



```shell

python tools/train.py configs/algorithms/erm/lbap_core_ec50_assay_erm.py

```                                                        



If 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: 



```shell

python tools/train.py configs/algorithms/erm/sbap_core_ec50_assay_erm.py

``` 



## Reference



:smile: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}

}

```     



## Disclaimer 

This is not an officially supported Tencent product.