| <div align="center"> |
| <img src="docs/logo.png" alt="BindCLIP logo" width="500"> |
| <h2> |
| A Unified Contrastive–Generative Representation Learning Framework<br> |
| for Virtual Screening |
| </h2> |
| </div> |
| |
| <p align="center"> |
| Official implementation of BindCLIP (<strong>KDD 2026 Oral</strong>) |
| </p> |
|
|
| <p align="center"> |
| <a href="https://dl.acm.org/doi/10.1145/3770855.3819040">📄 Paper</a> • |
| <a href="./LICENSE">📜 License</a> |
| </p> |
|
|
|
|
| <p align="center"> |
| <img src="docs/framework.jpg" alt="BindCLIP framework" width="950"> |
| </p> |
|
|
| # Requirements |
|
|
| ```bash |
| conda create -n bindclip python=3.10 |
| conda activate bindclip |
| conda install pytorch=2.0 pytorch-cuda=11.8 -c pytorch -c nvidia |
| # Verify PyTorch and CUDA installation |
| # python -c "import torch; print(torch.__version__); print(torch.version.cuda)" |
| |
| conda install pyg=2.3.1 -c pyg |
| conda install "rdkit=2022.09.5" openbabel tensorboard pyyaml easydict python-lmdb -c conda-forge |
| # [IMPORTANT]: RDKit 2022.09.5 is required. |
| # Using a different version may require reprocessing the training data. |
| |
| # Install Uni-Core |
| cd Uni-Core-main |
| python setup.py install |
| |
| # Additional dependencies |
| pip install tokenizers==0.13.3 |
| pip install ml-collections tensorboardX wandb |
| pip install ipython |
| pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+cu118.html |
| pip install torch-sparse -f https://data.pyg.org/whl/torch-2.0.1+cu118.html |
| pip install torch-cluster -f https://data.pyg.org/whl/torch-2.0.1+cu118.html |
| conda install biopandas |
| ``` |
|
|
| # Data |
|
|
| ## Training Data |
|
|
| We release the training data used in BindCLIP, together with the external molecular library used for negative augmentation: |
|
|
| https://drive.google.com/drive/folders/1F48zwXEtmAgwGGuKCXOMFR21U8fxEAJh?usp=sharing |
|
|
| The released training data contains two versions corresponding to the hard-negative construction strategies described in the paper: |
|
|
| - Random Sampling |
| - UniMol Retrieval |
|
|
| **NOTE:** We use the same training/validation split as DrugCLIP. The only difference is that we additionally provide a binding pose and a negative molecule for each pair. |
|
|
| ## Evaluation Data |
|
|
| We follow the DrugCLIP evaluation setup for virtual screening benchmarks, including: LIT-PCBA and DUD-E |
|
|
| The processed benchmark data can be downloaded from: |
|
|
| https://drive.google.com/drive/folders/1zW1MGpgunynFxTKXC2Q4RgWxZmg6CInV?usp=sharing |
|
|
| # Checkpoints |
|
|
| We release two pretrained checkpoints: |
|
|
| https://drive.google.com/drive/folders/1KKID5DU_hh2e5sE5Xmem10lfSy_qWwIE?usp=sharing |
|
|
| The released checkpoints correspond to two different negative sampling strategies used during training: |
|
|
| - Random Negative Augmentation |
| - Hard Negative Augmentation |
|
|
| We provide both versions because we observed that the random-negative model can sometimes exhibit competitive or even better generalization performance depending on the downstream application. We encourage users to evaluate both checkpoints and choose the one that works best for their specific use case. |
|
|
| **NOTE: Our checkpoints are fully compatible with the DrugCLIP inference code. Users can directly replace the DrugCLIP checkpoint with a BindCLIP checkpoint without any code modifications.** |
|
|
| # Train |
|
|
| ```bash |
| bash bindclip.sh |
| ``` |
|
|
| # Test |
|
|
| ```bash |
| bash test.sh |
| ``` |
|
|
| # Retrieval |
|
|
| ```bash |
| bash retrieval.sh |
| ``` |
|
|
| Example files (`pocket.lmdb` and `mols.lmdb`) can be found in the DrugCLIP Google Drive folder under the `retrieval/` directory. |
|
|
| # Citation |
|
|
| If you find this work useful, please consider citing: |
|
|
| ```bibtex |
| @inproceedings{qiao2026bindclip, |
| title={BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening}, |
| author={Qiao, Anjie and Wang, Zhen and Li, Yaliang and Rao, Jiahua and Yang, Yuedong}, |
| year={2026}, |
| booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2}, |
| pages = {11854–11865}, |
| numpages = {12} |
| } |
| ``` |
|
|
