The initiative combines new funding with existing datasets, computing infrastructure and measurement technologies. The Department of Energy plans to invest more than $500 million over five years in biological measurement, modeling and computation. NIH will contribute datasets and other resources developed through more than $500 million in previous federal investment, while Biohub will work with NIH to standardize that information for AI training.
Biohub had already committed $500 million to the Virtual Biology Initiative when it was announced in April 2026. Of that amount, $400 million is going toward technologies that expand how scientists measure biology, including cryo-electron tomography, large-scale microscopy and tools for manipulating biological systems. Another $100 million supports research outside Biohub.
The immediate goal is to build a broader picture of cellular biology by collecting different types of information about cells and how they respond to changes. That data would then be used to train and evaluate AI models capable of making biological predictions.
“An accurate predictive model of biology could dramatically accelerate scientific discovery by enabling scientists to perform experiments digitally. The insights that come from this could unlock a far greater understanding of disease and open up completely new paths for cures,” Biohub Head of Science Alex Rives said.
Rives said the challenge requires coordinated data generation across many organizations rather than a single research institution working alone.
“Because of this potential, the creation of a virtual cell is one of the most important challenges for the next era of science. It will require coordinated data generation efforts at a national and international scale, which is why these partners are coming together. We invite the worldwide scientific community to join us in this project,” he said.
The models could eventually be used to examine questions ranging from ageing and regeneration to the molecular mechanisms behind diseases such as Alzheimer’s. Further ahead, the goal is to develop systems capable of examining an individual disease and predicting its molecular causes and potential interventions.
The participating organizations are also working on common standards so data generated across different institutions can be used together. Biohub plans to create shared identifiers, common formats and a unified point of access for the resulting datasets.
Google DeepMind said the effort depends on producing far more open biological data than researchers currently have available.
“The quest to build a virtual cell is one of the great collective scientific challenges and key to understanding the mechanisms of life. We will not solve this challenge without open, experimental biological data at an unprecedented scale, showing how living cells behave and respond to changes,” said Pushmeet Kohli, vice president of AI for Science at Google DeepMind and chief scientist at Google Cloud.
“This investment in biological data generation will help create an open, standardized data commons, which will lay the foundations researchers around the world need to better model biology,” Kohli added.
Isomorphic Labs is also joining the initiative as a founding member, with President Max Jaderberg pointing to the need for larger and more varied datasets.
“Generating the data to solve predictive systems biology requires scaling past the limits of what any single organization can produce today,” Jaderberg said. “By joining the Virtual Biology Initiative as a founding member, Isomorphic Labs is helping build a massive, multimodal data foundation. This initiative will generate the data needed to push the industry closer to the next significant breakthrough for biology.”
The Department of Energy will contribute through its Genesis Mission, using resources that include supercomputing, advanced imaging, modeling and autonomous laboratories. NIH will participate through its Bio Genesis Mission, bringing together biomedical repositories, research programs and national data infrastructure.
Other organizations involved include the Allen Institute, Broad Institute, Gladstone Institutes, Human Cell Atlas, Human Protein Atlas and Wellcome Sanger Institute. Nvidia will provide accelerated computing infrastructure, software and technical expertise, while Renaissance Philanthropy is supporting efforts to expand funding for data generation.
The long-term objective is an open research resource that scientists can use to train predictive AI systems capable of testing biological hypotheses digitally. The project is aimed at narrowing the gap between computational models and the complexity of living systems, with laboratory experiments reserved for questions that the models indicate are most likely to produce useful results.
This analysis is based on reporting from Firstpost & Biohub.
Image courtesy of Biohub.
This article was generated with AI assistance and reviewed for accuracy and quality.
