Artificial Intelligence in Drug Repurposing: A Cost-Effective Solution for Rare Diseases

Shalu Jain

Maharaja Agrasen Himalayan Garhwal University

Pauri Garhwal, Uttarakhand, India

mrsbhawnagoel@gmail.com

Abstract

Rare diseases, often neglected due to limited commercial incentives, pose significant challenges in drug development. This manuscript explores the application of artificial intelligence (AI) to drug repurposing as a cost-effective solution for these conditions. By leveraging machine learning, network analysis, and in silico simulations, AI can accelerate the identification of novel therapeutic uses for existing drugs. This review synthesizes literature up to 2018, outlines a statistical analysis of drug repurposing case studies, and presents a methodological framework integrating AI tools. Our findings suggest that AI-driven approaches not only reduce time and cost in the discovery process but also enhance the likelihood of clinical success for rare diseases, offering promising avenues for future research and healthcare policy.

Keywords

Artificial Intelligence, Drug Repurposing, Rare Diseases, Machine Learning, In Silico Analysis, Cost-Effectiveness, Computational Biology

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