Drug Repurposing in the Era of Artificial Intelligence: From Molecular Insights to Clinical Translation
Year: 2026
Type: article
Abstract: Drug repurposing, also known as drug repositioning or reprofiling, is the strategy of identifying new therapeutic indications for existing, approved, or investigational drugs outside their original scope of use. This approach offers a faster, lower-cost, and lower-risk alternative to de novo drug discovery because repurposed candidates already possess established pharmacokinetic, pharmacodynamic, and safety profiles. This review summarises the pharmacological and biological mechanisms that underlie drug repurposing, including target receptor interaction, pathway modulation, genomic and proteomic insight, and disease-mechanism overlap, and outlines the growing role of computational approaches such as artificial intelligence, machine learning, and network pharmacology in candidate identification. Representative case studies from the COVID-19 pandemic, including hydroxychloroquine, remdesivir, tocilizumab, captopril, and azithromycin, illustrate how existing drugs were rapidly redeployed during a public health emergency, while examples such as mifepristone and metformin demonstrate the regulatory and intellectual property challenges that repurposing efforts continue to face. The review further discusses clinical, regulatory, and economic barriers that limit the translation of computationally predicted candidates into approved therapies. It is concluded that drug repurposing has evolved from an opportunistic, serendipitous practice into a structured, data-driven discipline, and that its full potential will depend on harmonised regulatory pathways, open-access clinical data sharing, and sustained public-private investment.
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212175687
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