Scientific discovery is driven by the iterative process of observation, hypothesis generation, experimentation and data analysis. Despite recent advancements in applying artificial intelligence (AI) to biology, no system has yet automated all these stages1–3. Here we introduce Robin, a multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate hypotheses, propose experiments, interpret experimental results and generate updated hypotheses, achieving a semi-autonomous approach to scientific discovery. By applying this system, we were able to identify promising therapeutic candidates for dry age-related macular degeneration, the major cause of blindness in the developed world4,5. Robin proposed enhancing retinal pigment epithelium phagocytosis as a therapeutic strategy, and identified and confirmed in vitro efficacy for ripasudil and KL001. Ripasudil is a clinically used Rho kinase inhibitor that, to our knowledge, has never previously been proposed for the treatment of dry age-related macular degeneration. To elucidate the mechanism of ripasudil-induced upregulation of phagocytosis, Robin then proposed and analysed a follow-up RNA sequencing experiment, which revealed upregulation of ABCA1, which encodes a lipid efflux pump and represents a possible novel target. All hypotheses, experimental directions, data analyses and data figures in the main text of this report were produced by Robin. As one of the first AI systems to autonomously discover and validate novel therapeutic candidates within an iterative lab-in-the-loop framework, Robin establishes a new paradigm for AI-driven scientific discovery.
