PickPocket Enables Binding Site Prediction at the Proteome Scale

Accurately identifying protein binding sites is essential for drug discovery, yet existing computational methods often struggle to balance precision, recall, and scalability. We introduce PickPocket, a deep learning model that integrates sequence-derived evolutionary embeddings from ESM-2 with geometric structural representations from GearNet to predict ligand-binding sites at the proteome scale. PickPocket leverages both residue-level sequence context and graph-based spatial relationships, enabling it to generalize across diverse protein families while maintaining high precision. Evaluated on the LIGYSIS benchmark, PickPocket outperforms state-of-the-art methods, achieving the highest F1 score (0.42) and maintaining a competitive MCC (0.37). PickPocket effectively predicts cryptic pockets, surpassing specialized models like PocketMiner even without explicit training on ligand-induced conformational changes. Our large-scale analysis of 356,711 proteins further demonstrates PickPocket’s ability to identify novel binding sites across human drug targets. By combining evolutionary and geometric learning, PickPocket represents a scalable, data-driven approach for structure-based drug discovery.