Biohub, the U.S. Department of Energy, the National Institutes of Health, and new funding partners have announced a major expansion of an international effort to generate and make accessible the data needed for predictive AI models of biology. Together, the organizations are investing $1.8 billion in funding, data, computation, and new measurement technology, the largest coordinated commitment to generating AI-ready biological data to date. The goal is an open resource for the research community that lays the foundation for a deeper understanding and, ultimately, treatment of human diseases.
The Department of Energy will invest more than $500 million over five years in lab measurement, modeling and computation toward the effort. The National Institutes of Health will coordinate datasets, repositories, and knowledge bases developed through more than $500 million in prior federal investment, and Biohub will work with NIH to standardize them for AI model training. Google DeepMind, Isomorphic Labs, and Meta are collectively investing $300 million in the Virtual Biology Initiative to build the technologies and multimodal datasets needed to create predictive models of life.
The initiative will expand cell response data to interventions across far more cell types and conditions than have yet been studied. Biohub's founding $500 million commitment anchors the work, with $400 million supporting new technologies such as cryo-electron tomography, microscopy capable of imaging millions to billions of cells in living tissue, and engineering tools for building and perturbing biology at molecular, cellular, tissue, and whole-organism levels. A further $100 million funds research outside Biohub.
Biohub head of science Alex Rives said an accurate predictive model of biology could let scientists perform experiments digitally and open new paths for cures, and invited the global scientific community to join the project.
Source: Hacker News · Summarized by HeadlinesBriefing