Google Puts Millions Into Zuckerberg’s Push to Build a ‘Virtual Cell’

Google DeepMind, Meta and Isomorphic Labs, the AI drug discovery startup, have committed a joint $300 million to Biohub, the nonprofit biomedical research organization founded by Mark Zuckerberg and Priscilla Chan. The reported funding matters because it places three major AI-linked actors behind a single platform: a research body that is trying to convert biology into machine-readable data sets for models that may eventually shape drug discovery, disease prediction and basic life-science research.

Foundations: Biohub Begins as a 2016 Biomedical Platform

Biohub was founded in 2016 as a nonprofit biomedical research organization. Its structure differs from a traditional pharmaceutical company or university department because it was built to host long-horizon science: bringing researchers, engineers and data infrastructure into one place. Biohub has also publicly described a broad ambition to prevent, cure or manage all diseases by 2100, a goal that requires tools more powerful than individual lab studies.

That founding model explains why AI funding companies are interested. Disease biology produces enormous data from cells, proteins, genes, imaging and clinical records, but the data are fragmented, expensive to clean and hard to compare across experiments. If an organization can create shared, well-curated biological data sets, it becomes useful to model builders as well as to researchers.

Development: A $1.8 Billion Push Toward AI-Ready Biology

The $300 million investment is part of a larger $1.8 billion initiative to build AI data sets for biomedical science. The stated aim is to let AI systems “ask, predict, and answer biological questions digitally.” That phrase captures the shift from narrow tools, such as a single model for protein structure, toward broader biological reasoning systems trained on diverse experimental and computational evidence.

The initiative’s scale reflects a practical constraint in AI: model size alone is not enough for science. Biomedical models need high-quality training data, standardized annotations, reproducible benchmarks and access to measurements that span molecules, cells and tissues. A $1.8 billion dataset program is therefore not only a research grant; it is an attempt to create the raw material for future AI systems in life science.

Latest Progress: Three AI-Linked Funders Move Together

The funding was reported by Reuters and brings together three organizations with different AI strengths. Google DeepMind contributes frontier AI research capability. Meta brings experience in large-scale AI development and computing infrastructure. Isomorphic Labs adds a direct drug discovery perspective, because it is a startup focused on applying AI to therapeutic design.

Their joint commitment suggests that biomedical AI is moving beyond isolated partnerships. A model lab, a platform company and a drug discovery startup now share support for the same data infrastructure. The practical question is how Biohub will use the $300 million within the wider $1.8 billion effort, and how much of that spending will produce open or shared datasets rather than internal research assets.

Biohub is currently at a scaling stage. It began in 2016 as a nonprofit science organization, has grown into a data-centric biomedical platform, and now sits at the center of a $1.8 billion AI dataset initiative backed by a $300 million joint investment from Google DeepMind, Meta and Isomorphic Labs.

Meta description: Google DeepMind, Meta and Isomorphic Labs invest $300 million in Biohub, part of a $1.8 billion AI biomedical dataset effort linked to its 2016 founding.

Tags: Biohub, Google DeepMind, Meta, Isomorphic Labs, AI biomedical data

Featured image: abstract technology style with molecular data grids and light trails, no real people, no company logos, no text.