Asian Journal of Pharmaceutical Research

  • Year: 2025
  • Volume: 15
  • Issue: 1

From Bench to Bedside: AI-Enabled Drug Repurposing for Innovative Therapies in Complex Diseases

  • Author:
  • Karina Laxman Yadav1,*, Neha Desai2, Anuradha Prajapati2, Sachin Narkhede2, Sailesh Luhar2
  • Total Page Count: 5
  • Page Number: 72 to 76

1Smt. B.N.B. Swaminarayan Pharmacy College, Gujarat Technological University, Salvav, Vapi, Gujarat, India, 396191

2Department of Pharmaceutics, Smt. BNB Swaminarayan Pharmacy College, Gujarat Technological University, Salvav, Vapi, Gujarat, India, 396191

Abstract

This comprehensive review article gives the information about the importance about AI in drug repurposing for complex diseases. The increasing prevalence of complicated diseases such as infectious diseases, neurological disorders, and cardiovascular problems calls for the development of novel therapeutic approaches. Conventional medication development is expensive and time-consuming; years of study and clinical trials are frequently needed. AI-enabled medication repurposing has surfaced as a viable method to accelerate the process in this regard. This methodology leverages artificial intelligence (AI) technology, including machine learning and natural language processing, to explore the possibilities of currently licensed drugs for novel therapeutic applications. The present level of AI-driven medication repurposing is examined in this review, with an emphasis on the benefits it offers over conventional techniques and how quickly it could lead to the discovery of new treatments. Key AI strategies, effective case studies, and the shift from preclinical to clinical research are covered. It also tackles difficulties including interpretability of models, data quality, and regulatory concerns. All things considered, AI-enabled drug repurposing is a revolutionary strategy for creating more efficient and reasonably priced treatments for complicated illnesses.

Keywords

AI-enabled drug repurposing, Complex diseases, Precision medicine, Machine learning, Deep learning, Network analysis