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Gemini for Science is a new collection of science tools and experiments to expand the scale and precision of scientific exploration.
Today science faces a paradox: our collective knowledge is growing so fast that it’s becoming harder for individual scientists to see the full picture. Scientific breakthroughs often rely upon making creative connections between data, but the time required to do this manually can take weeks or even months. AI can help eliminate this bottleneck and serve as a force multiplier for scientific work by handling complex tasks. This allows researchers to focus on identifying and tackling the most impactful scientific problems and directions that would drive progress. Gemini for Science experimental tools on Google Labs include three primary prototypes designed to handle such tasks: Hypothesis Generation, built with Co-Scientist, simulates the scientific method, collaborating with researchers to define challenges and then using a multi-agent “idea tournament” to generate, debate, and evaluate hypotheses, with claims deeply verified and supported by clickable citations. Computational Discovery, built with AlphaEvolve and ERA (Empirical Research Assistance), is an agentic research engine that solves the limitation of testing hypotheses by generating and scoring thousands of code variations in parallel. This enables scientists to test novel modeling approaches for complex fields like solar forecasting or epidemiology that would otherwise take months. Literature Insights, built with Google NotebookLM, helps understand scientific literature by searching and structuring results into tables with custom, searchable attributes for side-by-side analysis. Researchers can use chat to uncover nuances grounded in their curated corpus and create high-fidelity artifacts such as reports, slide decks, infographics, and audio and video overviews. It synthesizes findings, identifies research gaps, and uncovers areas of opportunity. Access to these experiments is gradually opening, and advanced AI capabilities are also being brought to enterprise organizations through Google Cloud. Companies like BASF and Klarna are already using AlphaEvolve to optimize supply chains and enhance machine learning models, respectively. Organizations like Daiichi Sankyo, Bayer Crop Science, and the U.S. National Labs are using Co-Scientist to accelerate research and tackle fundamental scientific challenges, demonstrating significant value. Validation papers for ERA and Co-Scientist have been published in Nature.
As part of Gemini for Science, Science Skills is being launched as a specialized bundle that integrates insights from over 30 major life science databases and tools, including UniProt, AlphaFold Database, AlphaGenome API, and InterPro. Utilizing these skills on agentic platforms like Google Antigravity allows researchers to perform complex and often manual workflows, such as structural bioinformatics and genomic analyses, in minutes rather than hours. Early testing by Google's research teams using Science Skills has demonstrated this speedup in practice, with complex analyses that normally take hours now completing in minutes. This acceleration has already led to novel insights about potential mechanisms for a rare genetic disease caused by mutations in the AK2 gene. Further information on using Science Skills in Google Antigravity is available on their dedicated use-case page.
Google's commitment to developing and deploying scientific tools responsibly is rooted in collaboration with the scientific ecosystem. They are working with over 100 institutions, including Stanford University on liver fibrosis, Imperial College London on antimicrobial resistance, and The Crick Institute, to validate new systems and tools. To ensure the integrity of AI-generated insights, a trusted tester community of PhD students, industry researchers, and Nobel laureates has been established to stress-test the systems against complex real-world challenges. Additionally, dedicated pilots have been created with leading scientific conferences like ICML, STOC, and NeurIPS to develop pioneering tools for agentic peer review and scientific validation, such as the experimental Paper Assistant Tool (PAT) and ScholarPeer. This work builds upon a long history of AI advancements, with specialized AI models like AlphaFold already assisting millions of researchers with challenges such as malaria vaccines and plastic-eating enzymes, and AlphaGenome helping identify disease drivers. These tools, along with Google Scholar, Earth Engine, Colab, MedGemma, Earth AI, and Gemini Deep Research, are essential for researchers to organize information and perform complex data analysis at scale. With the latest Gemini Deep Think release, Google continues to enhance its core model capabilities for complex scientific tasks, working towards a future where AI accelerates scientific progress and helps solve pressing societal challenges.