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

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

SUBMITTER: Shen A 

PROVIDER: S-EPMC11410380 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature