Biohub, the U.S. Department of Energy, the National Institutes of Health and private-sector partners have announced a coordinated $1.8 billion commitment of funding, data, computing resources and measurement technology to create foundational biological data for predictive AI models.
The initiative is intended to build an open resource that researchers can use to develop AI systems capable of predicting how biological systems and human cells respond to interventions.
The Department of Energy plans to invest more than $500 million over five years in laboratory measurement, modeling and computation.
NIH will coordinate datasets, repositories and knowledge bases generated through more than $500 million of previous federal investment, with Biohub working to standardize that information for AI model training.
Google DeepMind, Isomorphic Labs and Meta are collectively investing another $300 million in the Virtual Biology Initiative.
Biohub previously committed $500 million to the initiative, including $400 million for technologies such as cryo-electron tomography, advanced microscopy and biological engineering tools, plus $100 million for external research.
The broader effort aims to provide the data required to develop virtual biological models that could allow researchers to conduct portions of experiments digitally before moving into physical laboratories.
DOE will contribute through its Genesis Mission, drawing on resources including exascale supercomputers, X-ray and neutron scattering, cryo-electron microscopy and autonomous laboratories across the National Laboratory system.
NIH will contribute through its Bio Genesis Mission and existing biomedical repositories and research programs.
Other participating scientific organizations include the Allen Institute, Broad Institute, Gladstone Institutes, Human Cell Atlas, Human Protein Atlas and Wellcome Sanger Institute.
NVIDIA will contribute accelerated computing infrastructure, software and technical expertise, while Renaissance Philanthropy is helping expand funding for data generation.
KEY QUOTES:
“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. 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.”
Alex Rives, Head of Science at Biohub
“Generating the data to solve predictive systems biology requires scaling past the limits of what any single organization can produce today. 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.”
Max Jaderberg, President of Isomorphic Labs
“This partnership represents a critical step forward in leveraging artificial intelligence for public benefit. By 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 the unique AI models, tool development, and biological data capabilities of Biohub, we are setting a new standard for open science that will accelerate discoveries in both medicine and biotechnology.”
Dario Gil, DOE Under Secretary for Science
“By combining resources and expertise, we can accelerate the development of universal cell models with sufficient biological complexity to predict how any cell responds to an intervention. The return from these models could be broad and profound, resulting in substantially faster timelines for medical breakthroughs as compared with attempting to attain the same results through laboratory experiments alone.”
Nicole Kleinstreuer, Ph.D., NIH Deputy Director for Program Coordination, Planning, and Strategic Initiatives
“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. 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.”
Pushmeet Kohli, VP of AI for Science at Google DeepMind and Chief Scientist of Google Cloud
