A Review on the Convergence of Artificial Intelligence and Drug Discovery

Review Article

Abstract

The use of artificial intelligence (AI) and machine learning (ML) has accelerated a fundamental transformation in the field of drug discovery, addressing long-standing challenges of time, cost, and attrition rates. This paper presents a comprehensive analysis of the critical role AI now plays across the pharmaceutical research and development pipeline. Key AI-driven applications are detailed, including genomics- and proteomics-based target identification, high-throughput virtual screening for hit discovery, and generative models for de novo molecular design. Furthermore, the advancements in predictive modeling for absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are examined, alongside the strategic repurposing of existing drugs for new therapeutic indications. The discussion extends to the foundational elements of this technological shift, such as the crucial data sources, diverse molecular representation techniques, and the spectrum of ML algorithms from classical methods to advanced deep learning architectures like graph neural networks and transformers. Through an examination of recent case studies, the tangible impact of AI in accelerating discovery timelines is highlighted. Persistent challenges, including data quality, model interpretability, and the evolving regulatory landscape, are also critically assessed. The success and integration of AI in medicine discovery depends on the robust benchmarking, transparent validation, and seamless incorporation into experimental workflows, heralding a new era of precision medicine