Application and progress of artificial intelligence agents in drug development

Drug discovery faces formidable challenges including high technology, high costs, substantial risks, and prolonged development timelines, necessitating disruptive technologies capable of systematically improving efficiency, enhancing predictive accuracy, and reducing failure rates. Artificial intelligence (AI) agent—an emerging intelligent paradigm powered by large language models—holds significant potential to transform the entire drug development pipeline. Their core capability lies in performing autonomous reasoning, planning, and tool utilization directed at complex scientific objectives, thereby integrating and orchestrating multiple research stages and transitioning AI from a mere “tool” to an “active collaborator”. Through knowledge integration and hypothesis generation, AI agents can identify underexplored therapeutic targets and novel mecha-nisms of action. In parallel, they can automate complex tasks such as molecular design, optimization, and synthesis planning, and further close the loop between virtual design and physical experimentation by interfacing with automated experimental platforms. Moreover, AI agents are evolving toward higher-level paradigms, including the development of integrated drug design platforms and general-purpose biomedical agents. This review systematically summarizes the core architectures of AI agents, highlights their applica-tions across key stages of drug development, and discusses current limitations along with future directions, providing a reference for researches in related fields.