Two research papers describe how Google's Co-Scientist and nonprofit FutureHouse's AI tools can succeed at drug retargeting by developing and testing hypotheses
Both tools generate hypotheses; one goes on to analyze some of the data. — On Tuesday, Nature released two papers describing AI systems intended …
Ars TechnicaJohn Timmer
Context & Ripple Effects
Google’s biomedical AI effort has progressed from a selectively tested Gemini 2.0-based co-scientist tool in 2025 to experimental Gemini for Science tools that span hypothesis generation, testing, and literature understanding. Google Cloud had already positioned AI tools for biotech and pharmaceutical customers as a drug-discovery accelerator.
The Nature papers add published evidence around a narrower but commercially meaningful task: drug retargeting. FutureHouse’s inclusion also shows that the emerging category is not confined to Google’s platform strategy.
First-order effects
Google and FutureHouse gain research-backed validation that their systems can generate useful drug-retargeting hypotheses; at least one reported system also extends into data analysis.
Biomedical researchers evaluating these tools have a more concrete basis to use them as hypothesis-generation and analysis aids, rather than solely as literature-search interfaces.
Second-order effects
Drug-discovery AI vendors will face greater pressure to demonstrate performance on defined scientific workflows and to show how generated hypotheses connect to downstream analysis or testing.
Pharma and biotech buyers may increasingly compare tools on their ability to support specific retargeting and validation workflows, not simply on general-purpose model capability.
Third-order effects
If such results are repeatedly reproduced in real research settings, scientific AI is likely to be judged less as a standalone chatbot category and more as workflow infrastructure spanning literature, hypothesis formation, analysis, and experimental validation.
The key competitive divide may shift toward systems that can produce auditable, testable scientific outputs and integrate with researchers’ data and processes; publication evidence will matter, but does not by itself establish broad laboratory utility.
The trend: This is part of the shift from general generative AI for scientific assistance toward domain-specific systems expected to support and substantiate discrete research decisions.
Our paper “Accelerating scientific discovery with Co-Scientist” is published today in @Nature. Read it here: https://www.nature.com/... and learn more about our real-world partnerships on the DeepMind blog: https://deepmind.google/...
We're pleased to share our first @Nature paper: Robin is the first multi-agent system for discovery in biology that integrates novel hypothesis generation with experimental data analysis in one continuous workflow. In this study, our team, including ophthalmologist @agreeb66,
Lots of news today. Don't have energy to write a hit tweet, so here's list 1. Our work on doing lab-in-the-loop with agents was published in Nature 2. We announced our first research partnership with a pharmaceutical company 3. We made new persistent code-writing agent
Today, our research on Co-Scientist was published in Nature. It introduces a new multi-agent AI system built with Gemini that iteratively generates, debates, and evolves novel hypotheses for complex scientific problems. Hypothesis Generation introduced today at #GoogleIO📷 as [ima…
Got to play with a little of this before launch as well. My experience as a social scientist was that it was more bioscience focused right now, but I think Google has been the leading lab in releasing serious AI tools to accelerate science & expect to see them improve fast.
Out of all the announcements at @Google I/O today, this is the one closest to my heart - our foundational research on Co-Scientist was published in @Nature and we announced its broad availability via @GeminiApp for Science. When you are suffering from a disease, time is [video]
Gemini for Science! I've been saying this for some time: we have now entered a new phase in science, I call it Science 3.0, with research becoming completely AI-assisted! Google DeepMind is pushing this to the next frontier with very useful AI models and modules!
Congrats, team! 🥳 And this is my first @Nature paper and my first paper with my ex-boss @demishassabis as well! 🥳 (well, if we don't count for @GeminiApp papers lol)
The results of the research happening in my team @GoogleDeepMind have convinced me that the next era of scientific discovery will be aided by AI agents acting as force multipliers for human ingenuity. That's why I'm proud to introduce Gemini for Science - a collection of
A big day for multi-agent AI to accelerate biomedical discovery, hypothesis generation, designing experiments with proof points of new candidate drugs (cancer, fibrosis, macular degeneration, antimicrobial resistance, and more) 2 @Nature reports @GoogleDeepMind @FutureHouseSF