Group Ligands Docking to Protein Pockets
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929687664525312 |
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| author | Guan, Jiaqi Li, Jiahan Zhou, Xiangxin Peng, Xingang Wang, Sheng Luo, Yunan Peng, Jian Ma, Jianzhu |
| author_facet | Guan, Jiaqi Li, Jiahan Zhou, Xiangxin Peng, Xingang Wang, Sheng Luo, Yunan Peng, Jian Ma, Jianzhu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15055 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Group Ligands Docking to Protein Pockets Guan, Jiaqi Li, Jiahan Zhou, Xiangxin Peng, Xingang Wang, Sheng Luo, Yunan Peng, Jian Ma, Jianzhu Biomolecules Artificial Intelligence 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. |
| title | Group Ligands Docking to Protein Pockets |
| topic | Biomolecules Artificial Intelligence |
| url | https://arxiv.org/abs/2501.15055 |