PGBind: pocket-guided explicit attention learning for protein-ligand docking

Ao Shen*, Mingzhi Yuan*, Yingfan Ma, Jie Du, Manning Wang☨

Briefings in Bioinformatics (IF=6.8)

Abstract

As more and more protein structures are discovered, blind protein–ligand docking will play an important role in drug discovery because it can predict protein–ligand complex conformation without pocket information on the target proteins. Recently, deep learning-based methods have made significant advancements in blind protein–ligand docking, but their protein features are suboptimal because they do not fully consider the difference between potential pocket regions and non-pocket regions in protein feature extraction. In this work, we propose a pocket-guided strategy for guiding the ligand to dock to potential docking regions on a protein. To this end, we design a plug-and-play module to enhance the protein features, which can be directly incorporated into existing deep learning-based blind docking methods. The proposed module first estimates potential pocket regions on the target protein and then leverages a pocket-guided attention mechanism to enhance the protein features. Experiments are conducted on integrating our method with EquiBind and FABind, and the results show that their blind-docking performances are both significantly improved and new start-of-the-art performance is achieved by integration with FABind.