PocketNet: Ligand-Guided Pocket Prediction for Blind Docking

Matthew Masters, Amr H Mahmoud, Markus Alexander Lill

Abstract: We introduce PocketNet, a novel method for identifying ligand binding sites (LBS). Unlike current methods, PocketNet is tailored to identify the binding site specifically associated with the target ligand. With most protein targets having multiple binding sites, the selection process becomes ambiguous without specific ligand information. This limitation negatively impacts downstream applications such as docking and virtual screening. PocketNet addresses this challenge by combining the ouput of multiple LBS prediction tools and utilizing a deep neural network that incorporates ligand information to re-rank the sites. Our results demonstrate that PocketNet outperforms the latest methods for both pocket prediction and blind docking tasks.