AI agents in drug discovery: applications and case studies

• Agentic AI autonomously executes drug discovery workflows by combining language models with specialized tools for perception, computation, action and memory. • Real-world implementations achieve speed improvements, compressing literature analysis from weeks to minutes and assay development from months to hours. • Future integration enables self-driving labs and digital twins, shifting human focus from routine tasks to strategic decisions with appropriate governance frameworks. AI agents are emerging as transformative tools in drug discovery, with the ability to autonomously reason, act and learn through complicated research workflows. Building on large language models and specialized tools, these systems can integrate biomedical data, execute tasks, conduct experiments and iteratively refine hypotheses. We provide a conceptual overview of agentic AI architectures and illustrate their applications across key stages of drug discovery, including literature synthesis, automated protocol generation, toxicity prediction, small-molecule synthesis, drug repurposing and end-to-end decision-making. Early implementations demonstrate substantial gains in speed, reproducibility and scalability. We discuss the challenges related to data heterogeneity, system reliability, privacy, benchmarking and outline future directions toward technology in support of science and translation.