Press release by Biohub: “…the U.S. Department of Energy, the National Institutes of Health, and new funding partners today announced a major expansion of an international effort to generate and make accessible the data enabling 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 result will be an open resource for the research community that provides the foundation for greater understanding and ultimately treatment of human diseases.
As part of this announcement, Biohub has partnered with the Department of Energy (DOE) Office of Science and the National Institutes of Health (NIH) to advance the frontier of artificial intelligence in biology. DOE will invest more than $500 million over five years in lab measurement, modeling and computation toward the international effort to build an AI-ready open data resource. NIH will coordinate the contribution of relevant datasets, repositories, and knowledge bases developed through more than $500 million in prior federal investment aligned to this initiative. Biohub will work with NIH to standardize these datasets for AI model training.
In addition, Google DeepMind, Isomorphic Labs, and Meta are collectively investing $300 million in the Virtual Biology Initiative to create the technologies and multi-modal datasets needed to build predictive models of life.
These datasets will enable the global scientific community to collectively build and use AI models that allow researchers to ask, predict, and answer biological questions digitally, accelerating the path to new ways of preventing and treating diseases. This initiative will deliver the foundational measurements to train these models, expanding cell response data to interventions across far more cell types and conditions than have yet been studied, and building and validating technologies for studying cells and cellular interactions at greater scale, speed, and accuracy.
The scale of the challenge explains why no single institution is attempting it alone. Modern AI models in biology, from protein structure prediction to whole-cell simulators, are ultimately limited by the quality and breadth of the experimental data used to train them. Today’s datasets capture cell responses to interventions across only a small fraction of the cell types and conditions that matter in human health. The Virtual Biology Initiative is designed to close that gap by coordinating data generation across institutions and disciplines, expanding cell response measurements to far more cell types and conditions than have yet been studied, and building and validating technologies capable of studying cells and their interactions at greater scale, speed, and accuracy. The result, organizers say, will be a foundational, openly accessible dataset that no laboratory, company, or government agency could produce on its own…(More)”.
