PPDock: Pocket Prediction-Based Protein-Ligand Blind Docking

Predicting the docking conformation of a ligand in the protein binding site (pocket), i.e., protein-ligand docking, is crucial for drug discovery. Traditional docking methods have a long inference time and low accuracy in blind docking (when the pocket is unknown). Recently, blind docking techniques based on deep learning have significantly improved inference efficiency and achieved good docking results. However, these methods often use the entire protein for docking, which makes it difficult to identify the correct pocket and results in poor generalization performance. In this study, we propose a two-stage docking paradigm, where pocket prediction is followed by pocket-based docking. Following this paradigm, we design a new blind docking method based on pocket prediction (PPDock). Through extensive experiments on benchmark data sets, our proposed PPDock outperforms existing methods in nearly all evaluation metrics, demonstrating strong docking accuracy, generalization ability, and efficiency.