AI in Drug Discovery and Development

Artificial intelligence (AI) is revolutionizing the landscape of drug discovery and development by accelerating timelines, reducing costs, and improving the precision of therapeutic design. From target identification to lead optimization and clinical trial design, AI-driven approaches—such as machine learning, deep learning, and natural language processing—are enhancing the efficiency and predictive power of each stage in the pharmaceutical pipeline. This review explores the current applications of AI across the drug development lifecycle, with a focus on virtual screening, de novo drug design, biomarker discovery, and patient stratification. We also discuss the challenges and limitations of integrating AI into biomedical research, including data quality, model interpretability, and regulatory considerations. By highlighting recent breakthroughs and emerging trends, this paper underscores the transformative potential of AI to redefine how new drugs are discovered, tested, and brought to market.