Closed-loop agentic AI in drug discovery.

Abstract

Drug discovery is undergoing a paradigm shift from isolated artificial intelligence (AI) tools to integrated, closed-loop, agent-driven systems that combine prediction with experimental execution. Recent advances in large language models (LLMs), generative frameworks, and self-driving systems are facilitating the emergence of adaptive, multi-agent ecosystems capable of hypothesis generation, iterative optimisation, and autonomous decision-making. Despite this progress, key challenges, including data bias, limited interpretability, coordination fragility, and regulatory misalignment, constrain the translation into reliable clinical outcomes. Here, the evolving landscape of agentic drug discovery is critically examined, illustrating the transition toward hybrid human AI intelligence, digital twins, and regulation-ready autonomous platforms. Resolving these challenges will determine whether the field remains tool-driven or advances into a truly self-evolving scientific enterprise.