Advancing Protein-Ligand Binding Site Prediction with 3D Instance Segmentation Techniques

This study introduces ISBNet-Pocket, a novel method for ligand binding site prediction (LBSP) that utilizes 3D point cloud instance segmentation to address the limitations of clustering-based postprocessing in many LBSP models. ISBNet-Pocket represents proteins as 3D point clouds, enabling precise identification and separation of binding pockets without relying on clustering algorithms. This approach improves prediction accuracy, particularly for overlapping or closely located binding pockets, by directly localizing the ligand binding sites and their comprising atoms. Furthermore, a hybrid clustering strategy was tested to accommodate spatially proximate pockets, revealing both metric improvements and the inherent unreliability of clustering as a post-processing step. ISBNet-Pocket demonstrates performance consistent with state-of-the-art LBSP techniques while eliminating the need for clustering. Future research will refine the method and explore its application in molecular docking studies.