Biohub Secures $1.8 Billion to Build AI-Powered Virtual Cell for Disease Research

Major Cross-Sector Collaboration Targets AI Biology Breakthrough

Biohub, the nonprofit biomedical research organization founded by Mark Zuckerberg and Priscilla Chan in 2016, announced a massive $1.8 billion collaboration to generate foundational data for AI models that predict and treat disease1

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. This represents the largest coordinated commitment to generating AI-ready biological data to date, bringing together tech giants, federal agencies, and scientific institutions in an unprecedented push to accelerate scientific discovery5

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Google DeepMind, Meta, and AI drug discovery startup Isomorphic Labs are collectively investing $300 million into the Virtual Biology Initiative1

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. The U.S. Department of Energy will contribute more than $500 million over five years in laboratory measurement, modeling, and computation through its Genesis Mission2

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. The National Institutes of Health will coordinate datasets and repositories built with more than $500 million in earlier federal funding, which Biohub will standardize for AI training3

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. These commitments follow Biohub's own $500 million pledge announced in April 20262

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Building the Virtual Cell Through Digital Experimentation

The initiative aims to create a virtual cell that researchers can use to carry out simulations and perform experiments digitally1

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. Alex Rives, Biohub's head of science, emphasized that an accurate predictive model of biology could dramatically accelerate scientific discovery by enabling scientists to conduct digital experimentation5

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. The Virtual Biology Initiative will measure how cells respond to changes across far more conditions than scientists have studied so far, then use that data to build predictive models of disease that could compress drug development timelines currently taking years2

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Current cell datasets contain hundreds of millions of cells, but an accurate predictive model will require billions and eventually trillions of cells, according to Rives2

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. Priscilla Chan noted that biology has been a discovery-based science until this point, and the team has always held this as a community asset to build upon itself over time2

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. The work would normally take decades, but partners aim to compress it into five years, with a first dataset ready in about a year and accurate predictive models within five years2

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Advanced Technologies and Open Datasets Drive Research

Biohub's $400 million technology investment supports new measurement capabilities including cryo-electron tomography, which resolves near-atomic detail inside cells, and microscopy that can image millions to billions of cells in living tissue5

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. The Department of Energy will draw on exascale supercomputing, X-ray and neutron scattering, cryo-electron microscopy, and autonomous laboratories across the National Laboratory system3

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. Data will come from techniques including spatial transcriptomics, which maps molecular activity inside intact tissue, and screens that record how cells respond to environmental changes2

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The datasets will eventually become open datasets released publicly, though commercial funders get a head start2

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. Companies funding the initiative receive one year of exclusive access before data becomes available as a public scientific resource3

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. Rives explained that the embargo period creates incentive for commercial players while ensuring data rapidly becomes available broadly to scientific efforts4

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. Government-funded work carries no such restrictions2

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New Scientific Paradigm Promises Medical Breakthroughs

Rives described the effort as the beginning of a new scientific paradigm with AI, noting that the big challenge in AI biology is bridging the gap between the digital world and the physical world of biology through data4

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. The goal is to use AI to explore scientific questions virtually, allowing researchers to reserve expensive lab work for experiments most likely to yield important insights4

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. Such models could eventually help scientists investigate fundamental questions about aging and regeneration, or medical questions like which molecular mechanisms are responsible for Alzheimer's disease4

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Max Jaderberg, President of Isomorphic Labs, stated that generating data to solve predictive systems biology requires scaling past the limits of what any single organization can produce today5

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. The collaboration includes the Allen Institute, Broad Institute, Gladstone Institutes, Wellcome Sanger Institute, Human Cell Atlas, and Human Protein Atlas consortia3

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. Nvidia will provide computing and software, while Renaissance Philanthropy is helping raise additional funding3

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. Biohub plans to approach pharmaceutical companies and philanthropies next to expand the initiative2

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. Other AI labs including Anthropic and OpenAI Foundation are pursuing similar biology initiatives, with Anthropic building a wet lab and OpenAI starting a grant program exceeding $125 million for biological datasets2

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