THE IMPACT OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ON NEXT-GENERATION DRUG DISCOVERY.

Abstract

The enduring difficulties of conventional drug discovery, which are marked by exorbitant expenses, protracted timelines, and poor success rates, may be revolutionary addressed by artificial intelligence (AI) and machine learning (ML). This comprehensive review critically analyze recent advancements (2019–2025) in AI/ML techniques across the whole drug discovery process, from target selection to clinical development. We look at a variety of AI methods, such

as transformers, deep learning, and graph neural networks, with an emphasis on how they are used in crucial domains like preclinical safety evaluation, target identification, lead finding, and hit optimization. The benefits, drawbacks, and real-world difficulties are shown by our thorough comparison analysis. connected to several AI methodologies, highlighting crucial elements for effective application such data quality, model validation, and ethical considerations. The paper summarizes existing applications, points out enduring limitations, especially in clinical translation, interpretability, and data accessibility, and suggests future paths to fully realize AI's potential in developing safer, more effective, and more accessible medications. Through a focus on open and transparent methods, strong validation, and ethical frameworks, this review seeks to direct the ethical and significant incorporation of AI into pharmaceutical research and development