Google's Co-Scientist AI system generates novel hypotheses to accelerate scientific discovery

Accelerating scientific discovery with Co-Scientist - Nature

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and prior scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system's design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include: (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling; (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. While general purpose, we focus the validation in three biomedical applications: drug repurposing, novel target discovery , and explaining mechanisms of anti-microbial resistance . Specifically, Co-Scientist helped identify new drug repurposing candidates and synergistic combination therapies for acute myeloid leukemia, which were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI empowered scientists.

Co-Scientist: A multi-agent AI partner to accelerate research

Introducing a collaborative AI partner for researchers to develop new hypotheses in life sciences and beyond. Every great scientific breakthrough begins with a single, transformative idea. The spark of discovery relies on a researcher's ability to connect disparate facts and formulate the right hypothesis to test. But in an era of information overload and increasingly complex challenges, the search for these needle-in-a-haystack ideas has become a significant bottleneck for progress. We believe AI can help dramatically accelerate the pace of breakthroughs by serving as a dedicated partner in the generation and refinement of breakthrough scientific hypotheses. Today, in Nature we published our latest Co-Scientist research, introducing a new multi-agent AI system built with Gemini that iteratively generates, debates, and evolves novel hypotheses for complex scientific problems. We are making the Co-Scientist system available to individual researchers through Hypothesis Generation, a new experimental tool jointly developed across Google DeepMind, Google Research, Google Cloud and Google Labs. We'll begin rolling out in the coming weeks and researchers can register their interest at labs.google/science. Since sharing our early research last year, we've been developing and testing Co-Scientist together with teams who are leveraging it to tackle challenging problems - from antimicrobial resistance and plant immunity to liver fibrosis. We're excited to share some of the ways it is already being applied across fundamental biology, the natural sciences, and engineering.