Abstract
Significance Accurately identifying small molecule binding sites on proteins is fundamental to understanding protein function and enabling structure-based drug discovery, yet this critical step remains a major bottleneck in biomedical research and therapeutic development. Failures in virtual screening and lead optimization are often attributable to incorrect binding site identification rather than limitations in docking algorithms or scoring functions. We present YuelPocket, a unified graph neural network that overcomes this fundamental challenge by integrating both local and global protein–small molecule interactions within a single, scalable framework. YuelPocket achieves high predictive accuracy and offers a robust solution for precise binding site detection, providing a transformative tool for improving virtual screening and rational drug design.