Data-driven strategies for drug repurposing

Drug discovery is a complex, time-intensive, and costly process, often requiring more than a decade and substantial financial investment to bring a single therapeutic to market. Drug repurposing, the systematic identification of new indications for existing approved drugs, offers a cost-effective and expedited alternative to traditional pipelines, with the potential to address unmet clinical needs. In this study, we present a comparative analysis of drug–target interaction data from three extensively curated resources: ChEMBL, BindingDB, and GtoPdb, evaluating their release histories, curation methodologies, and coverage of approved drugs and targets. To facilitate therapeutic interpretation, we manually classified ChEMBL targets into 12 high-level biological families and mapped 817 clinically approved drug indications into 28 broader therapeutic groups. This structured framework enabled systematic profiling of the physicochemical properties of approved drugs across these groups. Leveraging this dataset, we established threshold values for 15 key physicochemical descriptors, including partition coefficient (alogP) and hydrogen bond donor/acceptor counts for each therapeutic group, providing practical benchmarks for indication-specific compound prioritization and filtering. We also analyzed cross-indication approvals to identify therapeutic areas with high repurposing potential. Furthermore, we introduce a pathway-based computational pipeline to predict repositioning opportunities for FDA-approved drugs across 10 major cancer types, which can be readily extended to other therapeutic domains. Collectively, our work integrates curated drug–target data, therapeutic-specific physicochemical profiling, and computational repurposing into a scalable, data-driven framework for accelerating drug discovery applications.