Biohub, DOE, NIH, and tech firms commit $1.8B to AI-ready biological data initiative

Major Investment in Biological AI Data

Biohub, the U.S. Department of Energy (DOE), the National Institutes of Health (NIH), and new funding partners announced a $1.8 billion commitment to expand an international effort. This funding will generate and make accessible data for predictive AI models of biology, representing the largest coordinated investment in AI-ready biological data to date.

Funding Contributions and Partnerships

The DOE will invest over $500 million over five years in lab measurement, modeling, and computation. The NIH will coordinate over $500 million in prior federal investment by contributing relevant datasets, repositories, and knowledge bases. Biohub will collaborate with NIH to standardize these datasets for AI model training.

Google DeepMind, Isomorphic Labs, and Meta are collectively investing $300 million in the Virtual Biology Initiative. This private sector contribution focuses on creating technologies and multi-modal datasets for building predictive models of life.

Purpose and Impact

These datasets will allow the global scientific community to build and use AI models for digital biological inquiry, accelerating disease prevention and treatment. The initiative will provide foundational measurements to train these models, expanding cell response data across more cell types and conditions, and developing technologies for studying cells at greater scale, speed, and accuracy.

Accelerating Scientific Discovery

The creation of an accurate predictive model of biology could significantly accelerate scientific discovery by enabling digital experimentation. This could lead to a greater understanding of disease and open new avenues for cures, according to Biohub Head of Science Alex Rives.