{"type":"ScholarlyArticle","title":"Bridging the Biophysical Gap: Holistic Environmental Awareness for 3D Linker Design","authors":[{"name":"Mengwei Sun","slug":"mengwei-sun","given":"Mengwei","family":"Sun","affiliation":""},{"name":"Chengwei Ai","slug":"chengwei-ai","given":"Chengwei","family":"Ai","affiliation":""},{"name":"Xiaoyi Liu","slug":"xiaoyi-liu","given":"Xiaoyi","family":"Liu","affiliation":""},{"name":"Shiqiang Ma","slug":"shiqiang-ma","given":"Shiqiang","family":"Ma","affiliation":""},{"name":"Qiaozhen Meng","slug":"qiaozhen-meng","given":"Qiaozhen","family":"Meng","affiliation":""},{"name":"Fei Guo","slug":"fei-guo","given":"Fei","family":"Guo","affiliation":""}],"abstract":"3D molecular linker design is a critical task in structure-based drug discovery, which requires the precise synthesis of chemical bridges to connect fragments within the constrained environment of a protein binding pocket. Existing methods often suffer from environmental blindness, treating the inter-fragment space as a vacuum and yielding candidates with poor binding affinity or severe steric clashes. To address this, we propose LinkerBridge, an equivariant framework that unifies biochemical semantics with physical constraints through two innovations: a Contextual Interaction-Aware Representation module that internalizes pre-existing biochemical semantics, and a Differentiable Physical Guidance mechanism derived from Van der Waals potentials to steer generation away from collision zones. Extensive evaluations on ZINC, GEOM, and BindingMOAD benchmarks, as well as Hsp90 and JNK3 case studies, demonstrate that LinkerBridge significantly outperforms state-of-the-art methods in real-world drug discovery.","doi":"10.24963/ijcai.2026/543","url":"https://ijcai.org/proceedings/2026/543","pdf":"https://ijcai.org/proceedings/2026/0543.pdf","markdown":"https://ijcai.org/proceedings/2026/543.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/543","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":"Machine Learning","track":"Main Track","number":543,"pages":{"first":4876,"last":4884},"keywords":["Multidisciplinary Topics and Applications: Bioinformatics","Multidisciplinary Topics and Applications: Health and medicine","Machine Learning: Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}