AI Powered Repurposing of Existing Drugs for Emerging Diseases.

Abstract

Artificial intelligence (AI) is revolutionizing therapeutic development by enabling rapid drug repurposing and precision medicine while reducing the cost, time, and risk of traditional approaches. AI models including machine learning, deep learning, and network-based frameworks integrate genomics, protein interactions, clinical records, and pharmacokinetic data to uncover novel drug–disease associations and prioritize repurposable candidates. This strategy has advanced treatments for neurodegenerative, rare, and complex CNS disorders such as multiple sclerosis, glioblastoma, and COVID-19 by predicting effective therapies.

AI-driven platforms link biological signatures with computational inference to reveal unexpected therapeutic matches, while precision medicine applications use electronic health records, neuroimaging, and multi-omics data to personalize treatment and optimize dosing. In oncology, AI accelerates compound design and nanotechnology-based delivery across barriers like the blood–brain barrier. Despite major progress through graph neural networks and generative models, challenges remain in data heterogeneity, interpretability, and regulation, requiring integration of computational and clinical validation for successful translation.