Google DeepMind, Meta and AI drug discovery company Isomorphic Labs will invest a total of $300 million, or 401.37 billion won, in a large AI biology project for disease research.

On Oct. 7, The Verge reported that the three companies will jointly invest $300 million in the Virtual Biology initiative led by the non-profit biomedical research organisation Biohub. The amount invested by each company was not disclosed. The project will marshal resources totalling $1.8 billion by combining funding, existing data, and computing and measurement technologies.

The central goal is to build a large-scale biological dataset that AI can use. Based on this, the project will develop virtual cell models to predict how cells respond to various interventions such as disease and drugs. It aims to shorten the time it takes for researchers to first run some biological experiments in a digital environment, identify the causes of disease and find candidate treatments.

Biohub announced the project in April and said it would invest $500 million, or 668.95 billion won. Of that, $400 million, or 535.08 billion won, will go to new measurement technologies such as cryo-electron tomography and large-scale cell imaging, and $100 million, or 133.77 billion won, will support external research.

The U.S. government is also participating. The Department of Energy will invest more than $500 million over the next five years to provide national laboratory supercomputers and imaging and modelling technologies. The National Institutes of Health will link biological datasets, repositories and knowledge bases it built with more than $500 million in existing investment and standardise them to make them suitable for AI training.

Alex Rives (알렉스 리브스), Biohub’s chief scientific officer, explained, "If an accurate biological predictive model is created, scientists will be able to do part of their experiments digitally and greatly increase the speed of discovery." He also described building virtual cells as a key task for next-generation science and stressed, "Large-scale data collaboration spanning countries, companies and research institutions is needed."