Group Ligands Docking to Protein Pockets

• URL: http://arxiv.org/abs/2501.15055v1
• Date: Sat, 25 Jan 2025 03:36:17 GMT
• Title: Group Ligands Docking to Protein Pockets
• Authors: Jiaqi Guan, Jiahan Li, Xiangxin Zhou, Xingang Peng, Sheng Wang, Yunan Luo, Jian Peng, Jianzhu Ma,
• Abstract summary: We propose textscGroupBind, a novel molecular docking framework that simultaneously considers multiple triangle docking to a protein.<n>We set a new S performance on the PDBBind blind docking benchmark, demonstrating the effectiveness of our proposed molecular docking paradigm.
• Score: 25.198533538897966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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