Global coalition commits $1.8 billion to build open data for AI models of biology

In what organizers describe as the largest coordinated investment ever made in generating artificial intelligence-ready biological data, Biohub, the U.S. Department of Energy, the National Institutes of Health, and a roster of new funding partners announced on October 7, 2026 a combined commitment of $1.8 billion in funding, data, computation, and new measurement technology. The effort, an expansion of the Virtual Biology Initiative first unveiled in April 2026, aims to produce an open resource that the global research community can use to train predictive models of living systems. The stated ambition is nothing less than a virtual cell: a computational model accurate enough that scientists could perform experiments digitally, predicting how any cell responds to an intervention before ever touching a pipette. If successful, the initiative could compress the timelines for understanding disease mechanisms and developing new therapies, transforming biology from a largely descriptive science into a predictive one.

The scale of the challenge explains why no single institution is attempting it alone. Modern AI models in biology, from protein structure prediction to whole-cell simulators, are ultimately limited by the quality and breadth of the experimental data used to train them. Today’s datasets capture cell responses to interventions across only a small fraction of the cell types and conditions that matter in human health. The Virtual Biology Initiative is designed to close that gap by coordinating data generation across institutions and disciplines, expanding cell response measurements to far more cell types and conditions than have yet been studied, and building and validating technologies capable of studying cells and their interactions at greater scale, speed, and accuracy. The result, organizers say, will be a foundational, openly accessible dataset that no laboratory, company, or government agency could produce on its own.

The federal contribution anchors the effort on two fronts. The Department of Energy’s Office of Science will invest more than $500 million over five years in laboratory measurement, modeling, and computation toward building the AI-ready open data resource. That investment flows through the Genesis Mission, a cross-agency initiative led by DOE, and draws on some of the most powerful scientific infrastructure in the world: exascale supercomputing, X-ray and neutron scattering facilities, cryo-electron microscopy and tomography, and autonomous laboratories distributed across the National Laboratory system. Darío Gil, DOE’s Under Secretary for Science, framed the partnership as a new standard for open science, pointing to the combination of DOE’s computing and measurement assets — including 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.

The National Institutes of Health, for its part, will coordinate the contribution of relevant datasets, repositories, and knowledge bases developed through more than $500 million in prior federal investment aligned to the initiative. Through its Bio Genesis Mission, NIH plans to bring together existing biomedical datasets, national data infrastructure, and research programs to help build AI-ready resources for the broader scientific community. The resources in play are substantial: national biomedical repositories catalogued by NIH’s National Library of Medicine and the National Center for Biotechnology Information, as well as NIH Common Fund programs that are already developing coordinated biological atlases, shared data standards, and AI-ready biomedical datasets. Biohub will work with NIH to standardize these datasets for AI model training, a step that many researchers consider as important as the raw data itself, since heterogeneous formats and inconsistent annotations have long hampered large-scale machine learning in biology.

Industry is contributing at significant scale as well. 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. Max Jaderberg, President of Isomorphic Labs, emphasized that generating the data required for predictive systems biology means scaling past the limits of what any single organization can produce today, and described the initiative as building a massive, multimodal data foundation intended to push the industry toward the next significant breakthrough in biology. Pushmeet Kohli, Vice President of AI for Science at Google DeepMind and Google Cloud’s Chief Scientist, argued that the quest to build a virtual cell is one of the great collective scientific challenges and cannot be solved without open, experimental biological data at an unprecedented scale showing how living cells behave and respond to change. In his view, the investment will help create an open, standardized data commons that lays the foundation researchers worldwide need to better model biology.

Biohub’s own founding commitment of $500 million anchors the scientific core of the program. Of that sum, $400 million supports new technologies that expand what biologists can measure. Among them is cryo-electron tomography, an imaging technique that resolves structures at near-atomic detail inside the cell, allowing researchers to observe molecular machines in their native cellular environment rather than in isolation. Another is advanced microscopy capable of imaging millions to billions of cells in living tissue, which would make it possible to capture how entire organs respond to perturbations at single-cell resolution. The remainder funds engineering tools to build and perturb biology at molecular, cellular, tissue, and whole-organism levels — the experimental manipulations that give AI models the cause-and-effect data they need to learn how biological systems respond to change. A further $100 million funds research outside Biohub, extending the reach of the program across the wider scientific community.

The initiative has also drawn in leading scientific institutions and consortia with experience organizing transformative international collaborations, from the Human Genome Project onward. The Allen Institute, Broad Institute, Gladstone Institutes, the Human Cell Atlas, the Human Protein Atlas, and the Wellcome Sanger Institute have come together to help nucleate the scientific community across academia and industry around developing effective strategies to maximize the impact of virtual biology. These groups are committed to working together as part of the Virtual Biology Initiative as well as through independent efforts toward the shared goal. NVIDIA will support the initiative by leveraging accelerated computing infrastructure, domain-specific software, and technical expertise, while Renaissance Philanthropy is helping to expand funding for data generation. The breadth of the coalition reflects a growing consensus that the bottleneck in computational biology is no longer algorithms but data.

Biohub’s role extends beyond generating measurements to building the connective tissue that lets disparate datasets work in a unified fashion: shared standards, common identifiers, and a single point of access. Equally important, the organization is working to build 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 approach builds on a track record from the past decade, during which Biohub has expanded the reach and impact of measurement technologies and open datasets through projects such as Tabula Sapiens, a cross-tissue map of human cell types; OpenCell, an atlas of protein localization and interactions; and Zebrahub, a developmental atlas of the zebrafish. It has also built and maintained community data infrastructure, including CELLxGENE and the CryoET Data Portal, platforms that have become widely used resources for the single-cell and structural biology communities.

For the researchers involved, the payoff of a validated virtual cell could be broad and profound. Nicole Kleinstreuer, NIH Deputy Director for Program Coordination, Planning, and Strategic Initiatives, noted that by combining resources and expertise, the partnership can accelerate the development of universal cell models with sufficient biological complexity to predict how any cell responds to an intervention. The return, she suggested, could be substantially faster timelines for medical breakthroughs compared with attempting to attain the same results through laboratory experiments alone. Alex Rives, Biohub’s Head of Science, put the vision in even starker terms: an accurate predictive model of biology could dramatically accelerate scientific discovery by enabling scientists to perform experiments digitally, unlocking a far greater understanding of disease and opening completely new paths for cures. He called the creation of a virtual cell one of the most important challenges for the next era of science, one that will require coordinated data generation at national and international scale — and he extended an open invitation to the worldwide scientific community to join the project.

The announcement lands at a moment when AI-driven biology is moving from demonstration to application, with structure prediction and molecular design already reshaping drug discovery. What has been missing, experts in the field broadly agree, is the kind of systematic, standardized, openly available measurement data that powered the revolution in language models and, more recently, in protein modeling. By committing $1.8 billion across government, industry, philanthropy, and academia — spanning funding, data, computation, and measurement technology — the Virtual Biology Initiative is betting that the next leap in medicine will come not from a single algorithmic breakthrough but from the patient, coordinated work of measuring life at scale and making the results universally available. Whether the bet pays off will depend on execution across dozens of institutions, but the scale of the commitment signals that the race to simulate biology has officially begun.

Subject of Research: A $1.8 billion international initiative to generate open, AI-ready biological data for predictive models of cells and disease

Article Title: International, cross-sector collaboration commits nearly $2 billion to build foundational data for AI models to predict and treat disease

Article References: International, cross-sector collaboration commits nearly $2 billion to build foundational data for AI models to predict and treat disease. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Biohub, Virtual Biology Initiative, virtual cell, AI in biology, Department of Energy, NIH, Google DeepMind, Isomorphic Labs, cryo-electron tomography, open data, single-cell biology, predictive models