The push to build an artificial intelligence-based virtual cell is getting a major boost with the US Department of Energy (DOE), National Institutes of Health, Google DeepMind, and Meta committing more than $800 million in new resources, on top of $500 million already pledged by Biohub.
On Oct. 7, Biohub, the organization behind the Virtual Biology Initiative (VBI) to create data for AI models, announced that it was partnering with the DoE and NIH. DoE will invest $500 million over five years to support laboratory measurements, AI modeling, and computation. NIH will contribute existing data resources and will work with Biohub to standardize datasets for use in training AI models.
Additionally, Google DeepMind, Meta, and UK-based AI-first drug discovery company Isomorphic labs are collectively contributing $300 million to the VBI.
In a statement, Biohub called it “the largest coordinated commitment to generating AI-ready biological data to date.” The result is intended to be an open data resource for researchers, a single point of access with a degree of standardization and uniformity.
“An accurate predictive model of biology could dramatically accelerate scientific discovery by enabling scientists to perform experiments digitally,” Biohub Head of Science Alex Rives said in a statement. “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.”
Biohub is the main philanthropic outlet for Facebook cofounder and Meta CEO Mark Zuckerberg. In April, Biohub launched the VBI with $500 million and partnerships to create data with the Allen Institute, Arc Institute, Broad Institute, and Wellcome Sanger Institute, as well as consortia including the Human Cell Atlas, Billion Cells Project, and the Human Protein Atlas. Nvidia, which makes graphics processing units used in AI computation, has also signed on to provide computing resources, AI development tools, and technical expertise.
The partnerships align Biohub’s VBI with DoE and NIH’s respective programs to push the pace of scientific discovery using AI.
DOE funding will be used for fundamental cell research — data collection, AI analytics, measurement and imaging, modeling, and computation — drawing on supercomputing, imaging, and other laboratory resources from the department.
The Energy Department’s computing resources and facilities at the Joint Genome Institute will combine with Biohub’s expertise to “[set] a new standard for open science that will accelerate discoveries in both medicine and biotechnology,” Darío Gil, DoE’s Under Secretary for Science, said in a statement.
NIH’s contributions include 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.
In a statement, Biohub claimed the NIH data resources represent the output of approximately $500 million in previously allocated federal funding.
Whether Google DeepMind, Meta, and Isomorphic Labs are providing cash or in-kind contributions isn’t clear. Isomorphic labs is a spinout from Google DeepMind.