---
license: mit
---

# Data for FlashAffinity

This repository hosts the datasets and pre-trained model checkpoints for the paper:  
**"FlashBind: Towards Accurate and Efficient Structure-based Virtual Screening"**.

The main code repository is available at: [🔗 GitHub: AIDD-Lab/FlashBind](https://github.com/AIDD-Lab/FlashBind).

## Note

* **Enzyme Dataset (ESIBank):** For the enzyme dataset (`ESIBank.tar.zst`), we have not uploaded the pre-computed **ESM3 representations** due to their massive storage size. Users can generate these embeddings locally using the scripts provided in our [GitHub repository](https://github.com/AIDD-Lab/FlashAffinity).
* **Affinity Dataset (SAIR):** We utilize the **[SAIR Dataset](https://huggingface.co/datasets/SandboxAQ/SAIR)** for binding affinity values. We provide a complete preprocessing pipeline to handle this data. To prepare the dataset, please run the following scripts in order:
    1. `renumber.py`
    2. `structure_lmdb.py`
    3. `filter.py`
    
    *Please ensure you update the input parameters within each script before execution.* **Warning:** Due to the massive scale of the data, the preprocessing workflow takes approximately **12 hours (half a day)** and requires roughly **4–5 TB** of storage space.

## Citation

If you find FlashAffinity useful in your research, please cite our work:

```bibtex
@article{jiang2025flashbind,
  title={FlashBind: Towards Accurate and Efficient Structure-based Virtual Screening},
  author={Jiang, Songlin and Chen, Yifan and Krishnan, Aarti and Zhang, Yu and Jin, Wengong},
  journal={bioRxiv},
}