Group Ligands Docking to Protein Pockets
This work addresses a key bottleneck in computational biology for drug discovery by improving docking accuracy through group-based modeling.
The paper tackled the problem of molecular docking by proposing GroupBind, a framework that simultaneously docks multiple ligands to a protein, achieving new state-of-the-art performance on the PDBBind blind docking benchmark.
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.