The U.S. government, Google and Meta are joining a major effort to use artificial intelligence (AI) to better understand biology and speed up medical research.
Nonprofit Biohub announced on October 7 that its Virtual Biology Initiative has expanded into a $1.8 billion effort involving government agencies, technology companies and research organizations. The project will focus on creating large, standardized biological datasets that can be used to train AI models.
The long-term goal is to build AI systems that can predict how cells behave when exposed to diseases, drugs or other changes. Researchers hope this could make it possible to conduct more biological experiments digitally before moving to physical laboratory testing.
Biohub Expands Virtual Biology Program With $1.8 Billion Commitment
Biohub, a nonprofit founded by Meta CEO Mark Zuckerberg and Priscilla Chan, originally committed $500 million to the Virtual Biology Initiative. The expanded program now brings together Biohub, the U.S. Department of Energy (DOE), the National Institutes of Health (NIH), Google DeepMind, Meta and Isomorphic Labs.
Together, the organizations will provide funding, biological data, computing resources and new measurement technologies. Biohub says the combined commitment of $1.8 billion is the largest coordinated effort so far to create biological data specifically for AI research.
The initiative is focused on building an open scientific resource that can eventually be used by researchers around the world.
Google, Meta Commit $300 Million to AI Biology Research
Google is participating through Google DeepMind, while Alphabet-backed drug discovery company Isomorphic Labs is also involved. Google DeepMind, Isomorphic Labs and Meta have jointly committed $300 million to the initiative.
The companies will work with Biohub to generate biological data that AI systems can use to understand cellular processes. This could eventually support models capable of predicting biological outcomes instead of simply identifying patterns in existing research.
The involvement of Google is significant because Google DeepMind has already invested heavily in AI biology research, including systems such as AlphaFold that predict the structures of proteins.
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DOE and NIH Strengthen U.S. Support for AI Biology
The U.S. Department of Energy will contribute more than $500 million over five years. The funding will support laboratory measurements, biological modeling and computing infrastructure needed to produce and process large amounts of biological information.
The NIH will also play an important role by coordinating biological datasets and repositories created through more than $500 million in previous federal investments. Biohub plans to standardize these datasets so they can be used more effectively to train AI models.
This work also connects with the broader U.S. government effort to use AI and advanced computing for scientific research through the Genesis Mission. The White House has identified predicting living systems and accelerating drug discovery as key areas for AI research.
Biohub Aims to Build a Virtual Cell With AI
One of the biggest ambitions behind the initiative is the creation of a virtual cell. A virtual cell would be an AI-based model capable of representing and predicting how biological systems respond to different conditions.
Instead of immediately conducting every experiment in a laboratory, researchers could potentially use such a model to predict which experiments are most likely to produce useful results. For example, researchers could eventually ask an AI model how a particular cell might respond to a drug or genetic change.
The technology is still far from achieving this goal. Building a reliable virtual cell requires huge amounts of biological data covering different cell types, conditions and responses. Biohub says it wants to generate enough data to help researchers move from simply observing biological systems to predicting their behavior.
AI Biology Effort Targets Faster Drug Discovery
Drug development is one of the main areas that could benefit from the project. Developing a new drug can require years of laboratory research and testing. Scientists have to understand how potential treatments interact with cells and biological systems before they can progress toward clinical trials.
AI models trained on large biological datasets could help researchers identify promising candidates earlier. The idea is not to replace laboratory research. Instead, AI could help scientists decide which experiments are worth conducting and reduce the number of unsuccessful experiments.
The initiative is also expected to support research into disease prevention and treatment by helping scientists understand how biological systems change under different conditions.
New Biological Data Will Become Public After Exclusive Access
A major part of the project is its focus on open biological data. Biohub says the initiative will ultimately create a resource that can be accessed by the wider research community. However, commercial partners will receive an initial period of exclusive access to newly generated datasets before they are made publicly available.
This approach could give companies an incentive to invest in the project while ensuring that the resulting data eventually becomes available to academic and other researchers.
The first major dataset is expected within about a year, while the broader effort aims to develop more capable predictive biological models over the next five years.
$1.8 Billion Effort Targets the Data Gap in AI Biology
The initiative reflects a growing belief that AI’s next major scientific challenge may be biology. AI models have improved rapidly in areas such as language, images and software because researchers have access to enormous amounts of digital data.
Biology is different because much of the information needed to understand living systems has to be generated through physical experiments. That makes biological data more difficult and expensive to collect. The $1.8 billion initiative is therefore focused on building the data and computing infrastructure needed to connect AI models with real biological systems.
If successful, the project could give scientists a new way to study diseases, test potential treatments and understand how cells respond to different conditions. However, creating a reliable virtual representation of biology remains a major scientific challenge. AI models will need to accurately reflect complex biological processes before predictions can be trusted in real-world research.
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AI Biology Research Is Expanding
The Biohub initiative is part of a broader increase in investment in AI for scientific research. The U.S. government has already included biological research and drug discovery in its Genesis Mission.
NIH’s Bio Genesis Mission aims to use AI and advanced computing to accelerate biomedical research and improve the path from scientific discovery to health applications. Technology companies and AI labs are also exploring ways to use AI for drug discovery, biological modeling and laboratory automation.
The $1.8 billion Biohub effort stands out because it brings together government funding, major technology companies and a large-scale effort to create shared biological data.