App 介绍

分子对接是药物设计中的一个关键步骤，即预测药物等小分子如何与目标蛋白质结合。Uni-Mol Docking V2在蛋白质-配体对接预测上取得重要突破。在最新的PoseBuster数据集上，Uni-Mol Docking V2预测的77.6%配体结合构象达到高精度标准（RMSD < 2.0 Å），在先前版本上大幅提升。此外，Uni-Mol Docking V2 生成的构象较先前版本更加合理，确保输出正确的几何形态与手性关系。

参考文献

[1] Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., ... & Ke, G. (2022, September). Uni-Mol: A Universal 3D Molecular Representation Learning Framework. In The Eleventh International Conference on Learning Representations.

[2] Corso, G., Stärk, H., Jing, B., Barzilay, R., & Jaakkola, T. (2022). Diffdock: Diffusion steps, twists, and turns for molecular docking. arXiv preprint arXiv:2210.01776.

[3] Buttenschoen, M., Morris, G. M., & Deane, C. M. (2024). PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chemical Science.

[4] Alcaide, E., Li, Z., Zheng, H., Gao, Z., & Ke, G. (2023, October). UMD-fit: Generating Realistic Ligand Conformations for Distance-Based Deep Docking Models. In NeurIPS 2023 Generative AI and Biology (GenBio) Workshop.

[5] Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., ... & Jumper, J. M. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 1-3.

[6] Alcaide, E., Gao, Z., Ke, G., Li, Y., Zhang, L., Zheng, H., Zhou, G. (2024). Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction. arXiv preprint arXiv:2405.11769.

引用格式

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@inproceedings{
  zhou2023unimol,
  title={Uni-Mol: A Universal 3D Molecular Representation Learning Framework},
  author={Gengmo Zhou and Zhifeng Gao and Qiankun Ding and Hang Zheng and Hongteng Xu and Zhewei Wei and Linfeng Zhang and Guolin Ke},
  booktitle={The Eleventh International Conference on Learning Representations },
  year={2023},
  url={https://openreview.net/forum?id=6K2RM6wVqKu}
}

@inproceedings{alcaide2023umd,
  title={UMD-fit: Generating Realistic Ligand Conformations for Distance-Based Deep Docking Models},
  author={Alcaide, Eric and Li, Ziyao and Zheng, Hang and Gao, Zhifeng and Ke, Guolin},
  booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
  year={2023}
}

@misc{alcaide2024unimol,
      title={Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction}, 
      author={Eric Alcaide and Zhifeng Gao and Guolin Ke and Yaqi Li and Linfeng Zhang and Hang Zheng and Gengmo Zhou},
      year={2024},
      eprint={2405.11769},
      archivePrefix={arXiv},
      primaryClass={q-bio.BM}
}