AI-Driven Drug Repurposing for Multidrug-Resistant Infections: Computational Rigor, Translational Realities, and Strategic Future Directions

Abstract

Antimicrobial resistance (AMR) is a rapidly escalating global health crisis, and multidrug-resistant (MDR) bacterial pathogens cause substantial morbidity, mortality, and economic burden. Conventional antibiotic-discovery pipelines remain slow, costly, and prone to high attrition, creating an urgent need for alternative strategies. Drug repurposing, which involves identifying new antimicrobial applications for approved or investigational agents, offers a pragmatic and cost-effective approach that leverages existing pharmacokinetic, safety, and formulation data. Advances in artificial intelligence (AI), including machine learning, deep learning, graph neural networks, and natural language processing (complemented by AI-adjacent computational approaches such as network pharmacology and molecular docking, which are treated here as distinct methods), have accelerated the high-throughput screening and prioritisation of repurposable compounds. This review critically evaluates AI-driven drug repurposing for MDR infections, addressing computational methodologies, data integration, experimental validation, translational bottlenecks, and regulatory considerations. The literature published between January 2020 and September 2026 was identified through searches of PubMed/MEDLINE, Scopus, and Web of Science, and each case-study drug is annotated for the role of AI and for the level of computational, in vitro, animal, and clinical evidence. We examine methodological limitations, including dataset bias, interpretability constraints, and pharmacokinetic gaps, and highlight the need for explainable AI and rigorous validation frameworks. Case studies of halicin, auranofin, phenothiazines, disulfiram, and clofazimine distinguish agents discovered by AI (halicin) from those whose antimicrobial activity was established by conventional screening or clinical use and illustrate both the promise and the translational hurdles of repurposed antimicrobials. We conclude by outlining strategic pathways for integrating AI predictions with experimental and clinical workflows, emphasising multidisciplinary collaboration to accelerate translation. By combining computational innovation with pharmacological and regulatory rigor, AI-driven repurposing constitutes a complementary strategy to de novo antibiotic discovery, with the potential to expand therapeutic options against MDR pathogens and strengthen global antimicrobial stewardship.