Self-Evolving Multi-Agent Systems for Pharmaceutical Discovery

Agentic AI is reshaping drug discovery by replacing static models with autonomous, self-improving pipelines. These systems combine large language models, generative chemistry, multi-agent reinforcement learning, simulations, and robotic labs to automate hypothesis generation, molecular design, virtual screening, synthesis, and testing. Closed-loop workflows accelerate decisions, improve prediction accuracy, and minimize redundant experiments. Generative agents create diverse, synthesizable molecules, while simulation agents strengthen ADMET and toxicity prediction. Robotic labs enhance reproducibility and reduce manual workload. Challenges remain in ensuring transparency, avoiding hallucinations, coordinating multi-agent tasks, and meeting regulatory expectations. This chapter outlines the architecture, benefits, and governance needs of agentic AI for faster, safer, and cost-effective drug discovery.

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