{"type":"ScholarlyArticle","title":"UniPocket: Physics-Aware Geometric Graph Learning with Manifold Completeness for Ligand-Specific Binding Site Prediction","authors":[{"name":"Kangxin Chen","slug":"kangxin-chen","given":"Kangxin","family":"Chen","affiliation":""},{"name":"Jieyu Zhao","slug":"jieyu-zhao","given":"Jieyu","family":"Zhao","affiliation":""},{"name":"Jinli Hu","slug":"jinli-hu","given":"Jinli","family":"Hu","affiliation":""},{"name":"Min Xie","slug":"min-xie","given":"Min","family":"Xie","affiliation":""}],"abstract":"Predicting ligand binding sites on protein surfaces requires capturing complex local geometries and satisfying physical constraints. Existing voxel-based methods suffer from high computational costs and rotation sensitivity, while standard point-cloud GNNs often lack geometric completeness—failing to distinguish chiral structures or subtle topological variations. In this work, we propose UniPocket, a novel E(3)-equivariant surface graph neural network. Inspired by recent advances in efficient geometric completeness, UniPocket constructs a Manifold-Aware Surface Encoder that utilizes local surface normals as virtual reference frames to capture complete geometric invariants without expensive high-order tensor products. Furthermore, we introduce a Ligand-Gated Message Passing mechanism to condition the surface features on the chemical semantics of the target ligand, and a Physics-Aware Vector Rejection Module that enforces steric constraints via orthogonal vector decomposition. Experimental results on standard benchmarks (PDBbind, COACH420, Holo4k) demonstrate that UniPocket achieves state-of-the-art performance, validating that geometric completeness and physical inductive biases are key to precise binding site detection.","doi":"10.24963/ijcai.2026/610","url":"https://ijcai.org/proceedings/2026/610","pdf":"https://ijcai.org/proceedings/2026/0610.pdf","markdown":"https://ijcai.org/proceedings/2026/610.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/610","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":610,"pages":{"first":5477,"last":5485},"keywords":["Multidisciplinary Topics and Applications: Bioinformatics"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}