AI in Drug Discovery: From Target Identification to Lead Optimization

ABSTRACT:

Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, addressing the limitations of traditional pharmaceutical research, including high costs, long timelines, and low success rates. By leveraging machine learning, deep learning, natural language processing, and generative models, AI enables efficient analysis of vast biomedical datasets and accelerates decision-making processes. This review provides a comprehensive overview of AI applications across the drug discovery pipeline, from target identification to lead optimization. It highlights computational techniques, real-world case studies, tools, and challenges associated with AI integration. Additionally, future perspectives on AI-driven personalized medicine and autonomous drug design systems are discussed. AI-driven innovations are poised to significantly reshape the pharmaceutical industry by improving efficiency, reducing attrition rates, and enabling precision therapeutics. [1–3]

Keywords: Artificial Intelligence, Drug Discovery, Machine Learning, Deep Learning, Target Identification, Lead Optimization, ADMET, Computational Pharmacology [4–6]