Artificial Intelligence in Drug Discovery and Development: Applications, Impact, and Challenges

Bringing a new medicine to patients remains one of the slowest and most expensive undertakings in applied science, typically requiring more than a decade and billions of dollars, with the great majority of candidates failing along the way. Artificial intelligence (AI) has emerged as a technology with the potential to change this arithmetic, and this review surveys how. We trace the application of machine learning and deep learning across the entire pipeline: target identification from multi-omic data; the prediction of protein structure, whose transformation by AlphaFold has reshaped structure-based design; virtual screening and computer-aided drug design; the generative de novo design of novel molecules; the prediction of absorption, distribution, metabolism, excretion, and toxicity; and applications in clinical development, pharmacology, and drug repurposing. Across these stages the recurring contribution of AI is the same: it compresses the design–make–test–analyse cycle by learning from data to predict which molecules and hypotheses are worth pursuing, allowing scientists to search an astronomically large chemical space with far greater efficiency. We review the accumulating evidence of impact, from AI-guided natural-product and anticancer discovery to therapeutic peptide design and the first AI-originated candidates now entering clinical trials, and we frame the economic logic through which even modest gains in success probability or cycle time translate into large savings. We then examine the challenges that temper the enthusiasm: the dependence on large, high-quality data; the interpretability and generalizability of models; the synthesizability of AI-proposed molecules; regulatory and validation questions; and the organizational and skills barriers to adoption.