{"type":"ScholarlyArticle","title":"MGRec: Structure-Grounded Medication Recommendation via Condition-Aware Molecular Representation Learning","authors":[{"name":"Jinke Feng","slug":"jinke-feng","given":"Jinke","family":"Feng","affiliation":""},{"name":"Wenjie Du","slug":"wenjie-du","given":"Wenjie","family":"Du","affiliation":""}],"abstract":"Medication recommendation plays a critical role in clinical decision-making by supporting personalized and safe treatment planning. Existing methods rely heavily on historical co-occurrence patterns and primarily optimize discrete prescription prediction objectives, limiting generalization in rare or emerging disease settings. We propose MGRec, a framework that shifts learning from discrete prescription prediction to condition-aware modeling in a continuous molecular representation space. MGRec treats molecular structure as the primary modeling target and employs a Therapeutic--Safety Factorized Conditional Variational Autoencoder to disentangle therapeutic and safety-related factors in a condition-aware molecular latent space. The model infers treatment-relevant molecular representations conditioned on current patient-specific clinical context, which are mapped to clinically approved medications for final recommendation. To improve clinical safety, we further introduce a DDI (drug-drug interaction)-guided latent regularization to integrate drug interaction knowledge at the representation level. Experiments on two real-world benchmarks demonstrate that MGRec achieves state-of-the-art accuracy and reduced interaction risk, particularly in data-sparse scenarios.","doi":"10.24963/ijcai.2026/289","url":"https://ijcai.org/proceedings/2026/289","pdf":"https://ijcai.org/proceedings/2026/0289.pdf","markdown":"https://ijcai.org/proceedings/2026/289.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/289","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":"Data Mining","track":"Main Track","number":289,"pages":{"first":2599,"last":2607},"keywords":["Multidisciplinary Topics and Applications: Health and medicine","Data Mining: Recommender systems","Machine Learning: Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}