Abstract

ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY AND DEVELOPMENT: A COMPREHENSIVE REVIEW

J. Supriya*

ABSTRACT

Background: The pharmaceutical industry is undergoing a profound transformation driven by the rapid integration of artificial intelligence (AI) and machine learning (ML) technologies. Traditional drug discovery and development processes are notoriously time-consuming, expensive, and associated with high failure rates, with an average cost of over $2.6 billion and 10-15 years to bring a single drug to market.[1,2] Objective: This review comprehensively discusses the role, applications, advantages, challenges, and future perspectives of artificial intelligence in various stages of drug discovery and development, including target identification, lead optimization, clinical trials, and drug repurposing. Methods: A systematic literature search was performed using PubMed, Google Scholar, Science Direct, and Web of Science databases. Articles published between 2015 and 2024 focusing on AI,machine learning, deep learning, and drug discovery were included and critically analyzed. Results & Conclusion: AI-powered platforms including deep learning, natural language processing, generative adversarial networks, and reinforcement learning have demonstrated remarkable capabilities in accelerating hit identification, predicting drug-target interactions, optimizing pharmacokinetic properties, and repurposing existing drugs. AI is revolutionizing pharmaceutical research and holds tremendous promise for delivering safer and more effective medicines faster and at lower cost.[3-12]

Keywords: Artificial Intelligence, Machine Learning, Drug Discovery, Deep Learning, Drug Development, Neural Networks, QSAR, Virtual Screening, Drug Repurposing, Pharmaceutical Sciences.