clorf6/FlashBind

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.

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.

• Affinity Dataset (SAIR): We utilize the SAIR Dataset 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:

@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},
}