AI IN DRUG DISCOVERY

Abstract

Drug discovery is a multifaceted and resource-intensive process traditionally marked by long development timelines, high financial costs, and significant attrition rates. The integration of Artificial Intelligence (AI) has introduced transformative opportunities to overcome these barriers by enhancing data-driven decision-making, accelerating molecular design, and improving clinical translation. This chapter explores the foundations, applications, and case studies of AI across all stages of drug discovery - from target identification and validation to lead optimization and clinical development. Real-world examples, including BenevolentAI, Atomwise, Insilico Medicine, and the Pfizer–IBM Watson collaboration, illustrate AI’s role in accelerating drug design, repurposing, and optimization. Despite its promise, challenges such as data quality, model interpretability, regulatory uncertainty, and intellectual property disputes continue to impede widespread adoption. The chapter concludes by emphasizing the future directions of AI in synergy with quantum computing, multi-omics integration, and personalized medicine. Overall, AI represents a paradigm shift in pharmaceutical innovation, poised to make drug discovery faster, more precise, and more cost-effective while promoting a human–AI collaborative ecosystem for sustainable biomedical advancement.