Biohub's virtual cell push grows to $1.8 billion, with the US DOE, NIH, Google DeepMind and Meta joining

The release spells out where the money comes from. DOE will invest more than $500 million over five years in lab measurement, modeling and computation as part of the cross-agency Genesis Mission it leads, drawing on exascale supercomputing, X-ray and neutron scattering, cryo-electron microscopy and tomography, and autonomous laboratories across the National Laboratory system. NIH will coordinate the contribution of relevant datasets, repositories and knowledge bases developed through more than $500 million in prior federal investment, and will work with Biohub to standardize those datasets for AI model training. In addition, Google DeepMind, Isomorphic Labs and Meta are collectively investing $300 million as they join the Virtual Biology Initiative.

Biohub describes the goal as building predictive models of biology, the virtual cell. The release says these datasets will let the global scientific community jointly 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 disease. In the words of Alex Rives, Biohub's Head of Science, an accurate predictive model of biology could dramatically accelerate discovery by enabling scientists to perform experiments digitally; the insights could unlock a far greater understanding of disease and open completely new paths for cures; and the creation of a virtual cell is one of the most important challenges for the next era of science, requiring coordinated data generation at national and international scale, which is why these partners are coming together. The release ends by inviting the worldwide scientific community to join.

The release also gives the effort's internal structure. The Virtual Biology Initiative, announced in April 2026, coordinates data generation across institutions and disciplines to build AI-ready open datasets. Biohub's founding $500 million commitment anchors that work: $400 million supports new technologies that expand what biologists can measure, including cryo-electron tomography that resolves near-atomic detail inside the cell, microscopy that can image millions to billions of cells in living tissue, and engineering tools to build and perturb biology at molecular, cellular, tissue and whole-organism levels, while a further $100 million funds research outside Biohub. That split explains why the announcement puts measurement and models in the same sentence: without new measurement tools there is no raw data to train a model on.

The participants go beyond the funders. Alongside DOE and NIH, scientific institutions and consortia with experience organizing transformative international collaborations, from the Human Genome Project onward, have joined: the Allen Institute, Broad Institute, Gladstone Institutes, Human Cell Atlas, Human Protein Atlas and Wellcome Sanger Institute, committed to the shared goal both through the initiative and through independent efforts. NVIDIA will support the effort with accelerated computing infrastructure, domain-specific software and technical expertise, and Renaissance Philanthropy is helping expand funding for data generation. Biohub itself is building the layer that lets these datasets work in a unified way, shared standards, common identifiers and a single point of access, while convening researchers across institutions and disciplines.

This is not starting from zero. Over the past decade Biohub has led Tabula Sapiens, OpenCell and Zebrahub, and built and maintained community data infrastructure including CELLxGENE and the CryoET Data Portal; the Virtual Biology Initiative builds on that experience to enable coordination at a scale no single institution could achieve alone. Max Jaderberg, President of Isomorphic Labs, says generating the data needed to solve predictive systems biology requires scaling past the limits of what any single organization can produce today. Darío Gil, DOE's Under Secretary for Science, stresses that combining DOE's exascale computing, experimental measurement and modeling assets, including premier user facilities at the Joint Genome Institute, the Environmental Molecular Sciences Laboratory and advanced structural beamlines, with Biohub's AI models, tool development and biological data capabilities sets a new standard for open science. Pushmeet Kohli, VP of AI for Science at Google DeepMind and Chief Scientist at Google Cloud, says the investment in biological data generation will help create an open, standardized data commons that lays the foundations researchers around the world need to better model biology.