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This study used machine learning to develop classifiers for identifying effective inhibitors of 5α-reductase isozyme 2, achieving high performance in distinguishing potent from weak inhibitors.

In this study, the researchers performed a comparative analysis of drug-target interaction data from multiple databases to refine drug repurposing strategies, revealing potential associations between drug characteristics and therapeutic groups, and predicting repositioning opportunities for FDA-approved drugs across major cancer types.

In this study, researchers evaluated drug-target interaction data from three major resources and developed a framework for drug repurposing, revealing associations between drug properties and therapeutic groups to aid in compound prioritization and predicting repositioning opportunities for existing drugs, particularly in cancer treatment.

This review discusses the data resources and computational models used in drug repositioning, highlighting their role in discovering unknown drug mechanisms and reports no new empirical results.

This review examines the current landscape of drug repositioning, highlighting that computational methods expand possibilities for reusing existing drugs to treat unmet medical needs. The authors discuss both the potential of these methods and the challenges faced in identifying new therapeutic applications.