A multi-agent system for automating scientific discovery
Ali Essa Ghareeb, Benjamin Chang, Ludovico Mitchener, Angela Yiu, Caralyn J. Szostkiewicz, Dmytro Shved, Gavin J. Gyimesi, Jon M. Laurent and 6 more
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AChecked 1 Oct 2026. Non-commercial or no-derivatives license: full text kept internally; only metadata and abstract are indexed and used.
| Source | License | Open-access status | Read as |
|---|---|---|---|
| openalex | cc-by-nc-nd | hybrid | Yellow |
| crossref | https://creativecommons.org/licenses/by-nc-nd/4.0 | — | Yellow |
| unpaywall | cc-by-nc-nd | hybrid | Yellow |
| europepmc | cc by-nc-nd | — | Yellow |
Abstract
BScientific discovery is driven by the iterative process of observation, hypothesis generation, experimentation, and data analysis. Despite recent advancements in applying artificial intelligence to biology, no system has yet automated all these stages [1, 2, 3]. Here, we introduce Robin, the first 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 (dAMD), the major cause of blindness in the developed world [4, 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 (ROCK) inhibitor that has never previously been proposed for treating dAMD. To elucidate the mechanism of ripasudil-induced upregulation of phagocytosis, Robin then proposed and analyzed a follow-up RNA-seq experiment, which revealed upregulation of ABCA1 , a lipid efflux pump and possible novel target. All hypotheses, experimental directions, data analyses, and data figures in the main text of this report were produced by Robin. As the first AI system 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.
Claims built on this paper
D- AI agents have already produced experimentally validated results in multiple domains: Boiko et al. 2023's Coscientist optimized palladium-catalysed cross-couplings, Ghareeb et al. 2026's Robin identified and confirmed ripasudil for dry age-related macular degeneration in vitro, and Swanson et al. 2024's Virtual Lab produced nanobodies with validated binding across SARS-CoV-2 variants. Trace →