This week, Mark Zuckerberg’s Biohub teamed up with Google DeepMind, Isomorphic Labs, Meta, and the federal government on a $1.8 billion effort to build an AI model that can predict how cells behave. The Department of Energy is committing more than $500 million over five years, NIH is contributing datasets built on more than $500 million in prior federal investment, the three commercial partners are putting in $300 million, and Biohub previously committed $500 million. In exchange, DeepMind, Isomorphic, and Meta get one year of exclusive access to the data before it is released publicly.
As we described in The Virtual Cell Revolution back in May, the promise of virtual cells is letting scientists test interventions virtually before running them in a lab, speeding up drug development and lowering costs. However, data remains an obstacle. As Biohub head of science Alex Rives put it, “The big challenge in biology is to bridge [the] gap between compute and the digital world and the real physical world of biology and life … The way to do that is through data.” The bet ultimately hinges on whether cellular biology follows the same scaling laws as the rest of AI, and Rives said he expects researchers to know within a year of the first large dataset if the effort is paying off.
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