Zuckerberg's Biohub leads a $1.8 billion push to build AI models that predict cell behavior
Quick Answer
Zuckerberg's Biohub is spearheading a $1.8 billion initiative to develop AI models for predicting cell behavior, enhancing drug development.
Quick Take
This effort includes contributions from Meta, Google DeepMind, and the US Department of Energy, with datasets expected to be standardized for AI training within a year.
Key Points
- Biohub's initiative includes a $500 million pledge for the 'Virtual Biology Initiative'.
- Meta, Google DeepMind, and Isomorphic Labs contribute $300 million to the project.
- The US Department of Energy invests over $500 million in lab measurements and compute.
- Commercial funders receive exclusive data access for one year before public release.
- First dataset expected to be ready in about a year.
📖 Reader Mode
~1 min readAI models are supposed to learn to predict cell behavior, which could speed up drug development. Biohub, the nonprofit backed by Mark Zuckerberg and Priscilla Chan, is coordinating a $1.8 billion effort spanning data, lab equipment, and compute, Reuters reports. The group had already pledged $500 million in April for its five-year "Virtual Biology Initiative." Meta, Google DeepMind, and Isomorphic Labs are contributing a combined $300 million, while the US Department of Energy is investing over $500 million in lab measurements and compute over five years. The National Institutes of Health are coordinating datasets built with more than $500 million in prior federal funding, and Biohub will standardize them for AI training.
Commercial funders get one year of exclusive access to the data they paid for before it goes public, according to Biohub research lead Alex Rives. Government-funded work will be available without those restrictions. A first dataset should be ready in about a year.
Other AI companies are also pursuing biology projects. Anthropic has built its own biology lab for AI-driven drug development, and the OpenAI Foundation is putting more than $125 million toward biological and medical datasets.
— Originally published at the-decoder.com
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