Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks

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 study found that DrugNet, a network-based prioritization method, effectively improves drug repositioning tasks, achieving high performance in validation tests and clinical trial comparisons.

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 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.