OTRec: A Deep Learning Recommender for Druggable Disease–Target Prioritization

Abstract Identifying druggable disease–target associations remains a central challenge in translational medicine, limiting therapeutic discovery and repurposing. Here, we present OTRec, a deep learning–based recommender system that ranks such associations at scale and evaluates them in a temporal hold-out setting. Unlike approaches that rely on manually curated or aggregated evidence scores, OTRec employs a two-tower architecture to learn latent representations from 663,351 disease–target pairs. The model integrates heterogeneous inputs, including textual descriptions, ontology-derived features, and biological annotations such as tractability, Gene Ontology (GO) terms, and pathway information. We perform temporal validation by training on the 2022 Open Targets (OT) release and evaluating on clinical trial data from 2025. OTRec improves on the retrospective OT association score (ROC-AUC: 0.872 ± 0.005 vs. 0.559; PR-AUC: 0.288 ±0.009 vs. 0.08). In 5 × 5 target-disjoint cross-validation, OTRec reaches ROC-AUC 0.950 and PR-AUC 0.844) improving on the OT evidence score (ROC-AUC 0.91; PR-AUC 0.45). We rank the druggable genome across ∼19,000 OT platform (OTP) diseases and release ∼ 282,500 candidate associations above a 0.65 score threshold (in-distribution CV precision 0.92), covering 4,346 diseases including 2,322 orphan diseases, through an interactive prediction platform. Anonymized demo, code, models, and predictions available: https://anonymous.4open.science/r/OTRec-D6DF/ Competing Interest Statement The authors have declared no competing interest. Footnotes Updated with additional results and evaluations