Artificial Intelligence in Drug Repurposing: Revolutionizing Drug Discovery through Computational Case Studies

Abstract

The application of artificial intelligence (AI) in drug repurposing has significantly reshaped the conventional landscape of drug discovery, allowing for the swift identification of new indications for existing medications. This review presents three notable case studies baricitinib for COVID-19, metformin in cancer therapy, and donepezil for Alzheimer’s disease and related neuroinflammatory conditions to showcase the transformative impact of AI technologies in this field. Utilizing tools such as knowledge graphs, machine learning algorithms, transcriptomic profiling, and pathway analysis, AI has enabled the accurate prediction of drug-target relationships, supported clinical hypothesis generation, and, in some instances, contributed to regulatory approvals. Nevertheless, challenges remain, including issues related to data quality, bias in observational datasets, limited mechanistic interpretability, and complex regulatory pathways. Despite these barriers, the outlook for AI-enabled drug repurposing remains optimistic, driven by continuous improvements in data harmonization, model transparency, and precision medicine strategies. The review emphasizes the importance of cross-disciplinary collaboration, rigorous validation standards, and regulatory adaptation to fully capitalize on AI’s potential in expediting drug development processes.Limitation of AI in drug repurposing