同时预测多个配体与蛋白结合构象，提升对接精度。
Group Ligands Docking to Protein Pockets
- 引入配体组交互层和三角注意力机制，联合建模多配体-蛋白关系。
- 在PDBBind盲测基准上达到新SOTA性能，验证方法有效性。
- 适合需要高效预测多个候选配体结合姿态的药物发现场景。
分子对接是计算生物学中的关键任务，近年来受到机器学习领域的广泛关注。现有方法通常将每个蛋白-配体对独立处理。受生物化学观察启发——同一靶点蛋白结合的配体倾向于采取相似构象，我们提出 extsc{GroupBind}，一种新型分子对接框架，可同时考虑多个配体与蛋白的对接过程。该方法通过引入配体组交互层和三角注意力模块，对蛋白-配体及配体组-配体对进行嵌入表示。结合基于扩散模型的对接方法，在PDBBind盲测基准上实现了新的SOTA性能，验证了所提对接范式的有效性。
原文摘要 · Abstract (English)
Molecular docking is a key task in computational biology that has attracted increasing interest from the machine learning community. While existing methods have achieved success, they generally treat each protein-ligand pair in isolation. Inspired by the biochemical observation that ligands binding to the same target protein tend to adopt similar poses, we propose \textsc{GroupBind}, a novel molecular docking framework that simultaneously considers multiple ligands docking to a protein. This is achieved by introducing an interaction layer for the group of ligands and a triangle attention module for embedding protein-ligand and group-ligand pairs. By integrating our approach with diffusion-based docking model, we set a new S performance on the PDBBind blind docking benchmark, demonstrating the effectiveness of our proposed molecular docking paradigm.
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