On Oct. 7, 2026, Biohub, the U.S. Department of Energy, the National Institutes of Health, and new funding partners announced a major expansion of an international effort to generate and make accessible the data enabling predictive AI models of biology. Together, the organizations are investing $1.8 billion in funding, data, computation, and new measurement technology, the largest coordinated commitment to generating AI-ready biological data to date. The result will be an open resource for the research community that provides the foundation for greater understanding and ultimately treatment of human diseases.

As part of this announcement, Biohub has partnered with the Department of Energy (DOE) Office of Science and the National Institutes of Health (NIH) to advance the frontier of artificial intelligence in biology. DOE will invest more than $500 million over five years in lab measurement, modeling and computation toward the international effort to build an AI-ready open data resource. NIH will coordinate the contribution of relevant datasets, repositories, and knowledge bases developed through more than $500 million in prior federal investment aligned to this initiative. Biohub will work with NIH to standardize these datasets for AI model training.

In addition, Google DeepMind, Isomorphic Labs, and Meta are collectively investing $300 million in the Virtual Biology Initiative to create the technologies and multi-modal datasets needed to build predictive models of life. 

These datasets will enable the global scientific community to collectively build and use AI models that allow researchers to ask, predict, and answer biological questions digitally, accelerating the path to new ways of preventing and treating diseases. This initiative will deliver the foundational measurements to train these models, expanding cell response data to interventions across far more cell types and conditions than have yet been studied, and building and validating technologies for studying cells and cellular interactions at greater scale, speed, and accuracy.

As the initiative takes shape, Biohub is bringing together partners across disciplines and industries to build the layer that lets their datasets work in a unified fashion — shared standards, common identifiers, and a single point of access. Equally important is building the scientific community around these resources — convening researchers across institutions and disciplines, connecting complementary expertise and capabilities, and creating opportunities to define and pursue ambitious scientific questions together. The Virtual Biology Initiative builds on these experiences to enable coordinated efforts at a scale that no single institution could achieve alone.