qizhipei/fabind

FABind: Fast and Accurate Protein-Ligand Binding (NeurIPS 2023)

Official Repository for the FABind Series Methods 🔥

Overview

This repository contains the source code for paper "FABind: Fast and Accurate Protein-Ligand Binding" and "FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation". If you have questions, don't hesitate to open an issue or ask me via [email protected], Kaiyuan Gao via [email protected], or Lijun Wu via [email protected]. We are happy to hear from you!

Note: if you want to install or run our codes, please cd to subfolders first.

FABind: Fast and Accurate Protein-Ligand Binding

Authors: Qizhi Pei^* , Kaiyuan Gao^* , Lijun Wu^† , Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan^†

FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation

Authors: Kaiyuan Gao^* , Qizhi Pei^* , Gongbo Zhang, Jinhua Zhu, Kun He, Lijun Wu^†

News

🔥May 27 2024: The training code, model checkpoint and preprocessed data for FABind+ are released!

🔥Apr 01 2024: Release our new version FABind+ with enhanced performance and sampling ability. Check the FABind+ paper on arxiv. The corresponding codes will be released soon.

🔥Mar 02 2024: Fix the bug of inference from custom complex caused by an incorrect loaded parameter and rdkit version. We also normalize the order of the atom for the writed mol file in post optimization. See more details in this commit.

🔥Jan 01 2024: Upload trained checkpoint into Google Drive.

🔥Nov 09 2023: Move trained checkpoint from Github to HuggingFace.

🔥Oct 10 2023: The trained FABind model and processed dataset are released!

🔥Oct 11 2023: Initial commits. More codes, pre-trained model, and data are coming soon.

About

Citations

FABind

@inproceedings{pei2023fabind,
  title={{FAB}ind: Fast and Accurate Protein-Ligand Binding},
  author={Qizhi Pei and Kaiyuan Gao and Lijun Wu and Jinhua Zhu and Yingce Xia and Shufang Xie and Tao Qin and Kun He and Tie-Yan Liu and Rui Yan},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023},
  url={https://openreview.net/forum?id=PnWakgg1RL}
}

FABind+

@article{gao2024fabind+,
  title={FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation},
  author={Gao, Kaiyuan and Pei, Qizhi and Zhu, Jinhua and Qin, Tao and He, Kun and Liu, Tie-Yan and Wu, Lijun},
  journal={arXiv preprint arXiv:2403.20261},
  year={2024}
}

Related

Awesome-docking

Acknowledegments

We appreciate EquiBind, TankBind, E3Bind, DiffDock and other related works for their open-sourced contributions.