Biohub-led initiative secures $1.8 billion for open biological data for AI models

Summary

Biohub, the U.S. Department of Energy and the National Institutes of Health are expanding the Virtual Biology Initiative with nearly $1.8 billion in combined funding, data, computing resources and measurement technology. The effort aims to create an open, standardized resource for training predictive AI models of biology, including models that can represent how cells respond to interventions and help researchers study disease digitally. DOE plans to invest more than $500 million over five years in cell research, data collection, AI analysis, imaging, modeling and computation, while NIH will coordinate relevant datasets, repositories and knowledge bases built through more than $500 million in earlier federal investment. Biohub will work with NIH to standardize those resources for model training. Google DeepMind, Isomorphic Labs and Meta are collectively investing $300 million in the Virtual Biology Initiative and will help develop the technologies and multimodal datasets required for predictive models of life. Biohub’s existing $500 million commitment includes $400 million for tools such as cryo-electron tomography, large-scale microscopy and biological engineering, plus $100 million for research outside Biohub. The initiative will draw on DOE exascale computing, advanced imaging, scattering facilities and autonomous laboratories, while NIH will contribute biomedical repositories, data infrastructure and coordinated biological atlases. NVIDIA will provide accelerated computing infrastructure, software and technical expertise. The partners describe the project as a long-term effort to establish shared standards, identifiers and access across datasets, with the stated goal of enabling broader scientific collaboration and eventually accelerating disease research and treatment development.