AI-Driven Drug Discovery: Accelerating the Development of Novel Therapeutics

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in the pharmaceutical industry, revolutionizing the drug discovery process. This comprehensive review explores the multifaceted applications of AI in drug discovery, from target identification to lead optimization and beyond. We examine the various machine learning algorithms, deep learning approaches, and natural language processing techniques that are reshaping the landscape of pharmaceutical research. The integration of AI with genomics, proteomics, and multi-omics data is discussed, highlighting its impact on target discovery and validation. We delve into AI-driven virtual screening, de novo drug design, and QSAR modelling, showcasing their roles in hit discovery and lead optimization. The paper also addresses the critical areas of predictive toxicology, ADMET profiling, and drug repurposing, where AI is making significant strides. Furthermore, we explore the implications of AI in precision medicine and personalized drug discovery, as well as the ethical considerations and regulatory challenges that accompany these advancements. Finally, we present emerging trends and future perspectives, including the potential of quantum computing and federated learning in collaborative drug discovery. This review provides a thorough analysis of the current state of AI in drug discovery, its challenges, and its promising future in accelerating the development of novel therapeutics.