Data-Driven Strategies for Drug Repurposing: Insights, Recommendations, and Case Studies

This study highlights the potential of drug repurposing as a cost-effective and faster alternative to traditional drug discovery by systematically identifying new uses for existing drugs. By analyzing drug–target interaction data from ChEMBL, BindingDB, and GtoPdb, the researchers classified targets and drug indications into biological families and therapeutic groups, respectively. This classification revealed associations between drug properties and therapeutic categories, aiding in compound prioritization for specific indications. The study also identified areas with high repurposing potential through cross-indication drug approvals and developed a computational pipeline to predict repositioning opportunities for FDA-approved drugs, particularly in cancer treatment. This data-driven framework aims to enhance drug discovery and translational applications.