Journal Article 2 Mentions
A multi-agent system for automating scientific discovery
Ali Essa Ghareeb•2026
Benjamin ChangLudovico Mitchener
Top 1% · 99th Percentile
65 citations · Computational Theory and Mathematics
Open Access

TLDR

A computer system helped researchers find existing medicines that may improve a damaged eye’s ability to clear waste. Laboratory tests supported two promising candidates, but much more testing is needed before either could become a treatment.

Summary

1 Study Aim

The authors aimed to build Robin, a multi-agent system (software agents with specialized roles) that could connect scientific reading, hypothesis generation, experiment planning, and data interpretation. They tested whether Robin could repeatedly suggest and refine treatment ideas using laboratory results. The study focused on dry age-related macular degeneration (dAMD), an eye disease with limited treatments. The authors also asked whether Robin could identify drug-repurposing opportunities and reveal biological mechanisms behind successful candidates. The study tested whether software could help people move from research questions to better treatment ideas more quickly.

2 Study Design

The authors built Robin from literature-search agents called Crow and Falcon, plus Finch, a data-analysis agent. The system reviewed about 551 papers, ranked disease mechanisms and drug candidates, and proposed laboratory tests. Scientists tested candidates in retinal pigment epithelium (RPE, cells supporting the retina) using flow cytometry (a method measuring properties of individual cells). Experiments used ARPE-19 cells and primary human RPE cells from one donor older than 60 years. The team tested five initial candidates, then ten additional candidates. Finch analyzed eight independent data-analysis runs per experiment. It also analyzed RNA-seq (RNA sequencing, which measures gene activity) data and completed a 170-question benchmark. The researchers combined computer analysis with human-run laboratory experiments.

3 Findings

The study showed that Robin selected increased RPE phagocytosis (cellular engulfment of material) as a treatment strategy. It identified ripasudil and KL001 as effective candidates in cell experiments. Ripasudil increased phagocytosis 1.89-fold in ARPE-19 cells and outperformed Y-27632. Ripasudil also worked in primary human RPE cells, while KL001 was another hit. Ripasudil is a ROCK inhibitor (a drug blocking Rho-associated kinase) already approved for glaucoma in Japan. RNA-seq revealed threefold higher ABCA1 activity, involving a protein that exports fats from cells. The authors estimate Robin reduced intellectual work from 359–424 hours to under two hours. Finch performed reliably on tested workflows but poorly on harder multi-step tasks. The authors recommend animal studies and clinical trials, while noting that Robin still needs precise protocols and stronger validation. The results suggest faster idea generation, not a ready-to-use treatment.

Abstract

Scientific 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.

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