An AI agent proposed new medical uses for established drugs nearly autonomously — uses that were supported by experiments on isolated human cells — with human input only to name diseases to be treated and run the AI-proposed lab experiments.
What’s new: Ali Essam Ghareeb, Benjamin Chang, and colleagues from the independent AI research lab FutureHouse, University of Oxford, and Fordham University released Robin, an open-source agent that proposes existing drugs to treat a given disease. Robin identified two drugs that were shown to address a biological mechanism behind dry age-related macular degeneration (dAMD), a leading cause of impaired vision. While Robin is freely available for noncommercial and commercial uses under the Apache 2.0 license, it relies on three earlier agents, two of which are proprietary. The literature-search agents called Crow and Falcon are free for research use only (bundled under the name Literature). The data-analysis agent Finch is available under an Apache 2.0 license.
How it works: For a given disease, Robin iteratively (i) identifies mechanisms behind the disease, (ii) designs experiments to affect the mechanisms, and (iii) finds existing drugs that address the mechanisms. Then (iv) humans run experiments in a lab, and (v) Robin analyzes the results. Robin uses OpenAI’s GPT o4-mini for most language-processing functions.
- Given a disease name, Robin comes up with questions about it. The Crow literature-search agent produces concise summaries of existing medical research and uses them to answer the questions. Based on the answers, Robin identifies 10 potential mechanisms that may contribute to the disease.
- For each mechanism, Crow searches relevant research and produces experimental designs that describe how to test that potential mechanism’s effects in the lab. Robin uses Claude 3.7 Sonnet to judge all the experimental designs in a pairwise manner to rank them.
- Given the top-ranked experimental design, Robin searches the web to find 30 drugs that act on the mechanism, focusing on drugs that are both commercially available (and therefore safety-tested) and haven’t been used previously to treat the disease. For each drug, the Falcon literature-search agent summarizes relevant research and produces a report that explains why the drug may work and any potential limitations. Robin used Claude 3.7 Sonnet to rank the drug reports in a tournament.
- Humans review the list and test top candidates in a lab, following the earlier experimental designs. Having completed the tests, they upload the test results and tell Robin to carry out a specific type of analysis.
- The Finch data-analysis agent carries out the analysis and produces a summary of its findings.
- Given those findings, Robin generates follow-up experiments, continuing the cycle until a human deems a drug candidate satisfactory.
Results: The authors ran their pipeline for dAMD. Robin hypothesized that increasing a process known as RPE phagocytosis, in which a particular type of cell in the eye removes pathogens and debris, could treat the disease. Robin proposed candidate drugs to boost RPE phagocytosis, of which two proved effective.
- In its initial run, Robin identified the research compound Y-27632 as a potential treatment. After humans tested the drug on eye cells, Robin noted a nearly 2x increase in the amount of RPE phagocytosis that occurred compared to the same test without the drug. A human follow-up analysis of the data got the same result.
- The team fed data from the initial run back into the pipeline. In the second run, Robin identified Ripasudil, a drug that’s approved in Japan to treat glaucoma, a condition that damages the optic nerve, as another candidate. In this test, the drug brought a 1.89x increase in RPE phagocytosis, and a human follow-up analysis showed a 1.75x increase. (The authors note that such analyses are challenging to automate because “the inherently ambiguous nature of biological data interpretation” can lead both humans and AI to reach varying conclusions in different runs.)
Yes, but: While the authors did test the drugs on eye cells, they did not test them on patients with the disease, so it is not yet known whether they work in living patients.
Behind the news: This work followed several studies dedicated to accelerating scientific research through agentic systems. One agentic system has generated machine learning research proposals as well as or better than humans. Another can generate a proposal, write and run code to test the proposal, and write the paper that describes the experiment and results. Google’s AI Co-Scientist, generated research proposals for biomedicine that humans later validated in the lab to treat acute myeloid leukemia.
Why it matters: The Robin pipeline not only generates hypotheses and proposes experiments, it also analyzes new experimental results and updates its hypotheses accordingly. Its combination of automation and iteration suggests that AI agents could streamline medical progress. Advances in robotics may enable AI models to carry out the necessary lab experiments as well, as previously demonstrated by RoboChem, a system in which an AI model controls a set of automated lab instruments. Similar approaches may be applicable to research areas beyond medicine.
We're thinking: It takes more than a decade and costs more than $1 billion to develop a drug in the United States, and the government approves only around 50 drugs a year. Finding new uses for drugs that are already approved, and new treatments for diseases that share underlying biological mechanisms, is an especially efficient use of time and funds.