Artificial intelligence (AI) in drug design and discovery: A comprehensive review

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in drug design and discovery, accelerating and enhancing various stages of pharmaceutical research and development. This comprehensive review explores the integration of advanced AI techniques including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Reinforcement Learning (RL), and emerging methods like Quantum Computing (QC), in key areas such as target identification, lead discovery, drug repurposing, lead optimization, and toxicity prediction. The review highlights the pivotal role of AI-driven models like AlphaFold in accurately predicting protein structures, revolutionizing our understanding of molecular interactions. Additionally, it discusses the growing use of AI in identifying novel drug repurposing candidates and optimizing therapeutic pipelines. Despite these advances, the review also addresses the methodological limitations, data-related challenges, and potential risks associated with AI applications in pharmaceutical R&D. Overall, the paper provides a critical overview of the current landscape, capabilities, and future directions of AI-powered drug discovery.