Sanger Institute joins international collaboration to build foundational data for AI models to predict and treat disease
Biohub, the U.S. Department of Energy (DOE), the National Institutes of Health (NIH), and other funding partners have announced a major expansion of an international effort to generate and make accessible the data enabling predictive artificial intelligence (AI) models of biology.
Together, the organisations are investing $1.8 billion in funding, data, computation, and new measurement technology. This is largest coordinated commitment to generating AI-ready biological data to date. The result will be an open resource for the research community that provides the foundation for greater understanding and treatment of human diseases.
Leading scientific institutions with experience in organising transformative international collaborations, from the Human Genome Project onwards, have come together to help maximise the impact of AI-powered biology. The groups include the Wellcome Sanger Institute, the Human Cell Atlas, the Human Protein Atlas, Allen Institute, Broad Institute, and the Gladstone Institutes.
These groups are committed to working together as part of the Virtual Biology Initiative as well as through independent efforts toward this shared goal.
As part of this announcement, Biohub has partnered with the U.S DOE, Office of Science and NIH to advance the frontier of AI in biology. DOE will invest more than $500 million over five years in lab measurement, modelling and computation toward the international effort to build an AI-ready open data resource.1
Through its Bio Genesis Mission, NIH will bring together existing biomedical datasets, national data infrastructure, and research programs to help build AI-ready resources for the broader scientific community, developed through more than $500 million in prior government investment.2 NIH’s extensive investments in biomedical research provide a foundation for this work. Biohub will work with NIH to standardise these datasets for AI model training.
In addition, 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 AI models. NVIDIA will also support the initiative to leverage accelerated computing infrastructure, domain-specific software, and technical expertise.3
These datasets will enable the global scientific community to collectively 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 diseases. This initiative will deliver the foundational measurements to train these models, expanding cell response data to interventions across far more cell types and conditions than have yet been studied. This includes building and validating technologies for studying cells and cellular interactions at greater scale, speed, and accuracy.
Biohub’s founding $500 million commitment anchors that work. $400 million will support new technologies that expand what biologists can measure: from near-atomic views inside cells to imaging millions of cells in living tissue and engineering tools that can build and perturb biology from molecules to whole organisms. A further $100 million funds research outside Biohub.
As the initiative takes shape, Biohub is bringing together partners across disciplines and industries to build the layer that lets their datasets work in a unified fashion, shared standards, common identifiers, and a single point of access.4 Equally important is building the scientific community around these resources, by convening researchers across institutions and disciplines, connecting complementary expertise, and creating opportunities to define and pursue ambitious scientific questions together.
“The Wellcome Sanger Institute is proud to contribute its expertise in genomics and large-scale data generation to this international effort. We want AI-powered discovery to benefit humanity. This initiative has the potential to advance biology at an unprecedented scale, improve health and identify new strategies to treat disease.”
Professor Muzlifah Haniffa, Deputy Director at the Wellcome Sanger
“This partnership represents a critical step forward in leveraging artificial intelligence for public benefit. By combining DOE’s exascale computing, experimental measurement, and modelling 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.”
Darío Gil, DOE’s 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 Program Coordination, Planning, and Strategic Initiatives (DPCPSI)
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
More information
- Techniques funded by the DOE’s investment will draw on exascale supercomputing, X-ray and neutron scattering, cryo-electron microscopy and tomography.
- NIH resources include national biomedical repositories catalogued by NIH’s National Library of Medicine (NLM) and the National Center for Biotechnology Information, as well as NIH Common Fund programmes that are already developing coordinated biological atlases, shared data standards, and AI-ready biomedical datasets.
- Renaissance Philanthropy is also helping to expand funding for data generation.
- Over the past decade, Biohub has expanded the reach and impact of measurement technologies and open datasets, leading projects such as Tabula Sapiens, OpenCell, and Zebrahub. It has also built and maintained community data infrastructure, including CELLxGENE and the CryoET Data Portal. The Virtual Biology Initiative builds on these experiences to enable coordinated efforts at a scale that no single institution could achieve alone.
