The initiative combines cutting-edge artificial intelligence with large-scale biological data, with the aim to transform how new treatments, drugs and therapies are developed.
Mark Zuckerberg and Priscilla Chan's Biohub have announced a new initiative that will look to combine artificial intelligence (AI) with cutting-edge biological research, potentially accelerating the pace of scientific discovery in human health and disease.
Since its inception in 2016, Biohub has brought together multidisciplinary teams of scientists and engineers to develop technologies that observe, measure and program biology at the cellular level. The organisation has collated the largest single-cell datasets globally and built specialised large-scale computing infrastructure dedicated to biological research.
Biohub is now launching the first large-scale scientific initiative specifically designed to advance AI for biological discovery. By integrating world-class computing power, pioneering AI research and state-of-the-art experimental and imaging capabilities, the project could transform the ways in which we understand and treat disease.
“When we started, our goal was to help scientists cure or prevent all diseases this century,” said Biohub co-founder Mark Zuckerberg. “With advances in AI, we now believe this may be possible much sooner. Accelerating science is the most positive impact we think we can make. So, we're going all in on AI-powered biology for our next chapter.”
Merging AI and biology
The initiative will be supported by EvolutionaryScale, a frontier AI research lab and public benefit company that has developed large-scale AI systems for the life sciences. Alex Rives, EvolutionaryScale’s co-founder and chief scientist, will serve as Biohub’s Head of Science, directing an integrated research strategy that spans experimental biology, data science and AI.
As we bring together frontier artificial intelligence, with biological data generation at scale, and creative experimental science, it may be possible to greatly accelerate the rate at which fundamental new scientific discoveries can be made.
“Advances in artificial intelligence are already starting to give us new tools to understand and engineer biology,” said Rives. “As we bring together frontier artificial intelligence, with biological data generation at scale, and creative experimental science, it may be possible to greatly accelerate the rate at which fundamental new scientific discoveries can be made.”
The combined team will develop the datasets, laboratory technologies and AI models needed to drive the next generation of biological research. Biohub plans to expand its computing capacity tenfold by 2028, reaching 10,000 GPUs, and is investing heavily in experimental biology and data generation.
Scientific grand challenges with drug discovery implications
Biohub has identified four ‘grand challenges’ that have significant implications for drug discovery:
- Developing a unified AI model of the cell to predict cellular behaviour.
- Advancing imaging systems to visualise complex biological processes at unprecedented scale.
- Creating instruments to monitor and modulate inflammation in real time.
- Using AI to reprogramme the immune system for early disease detection, prevention, and treatment.
“When I worked as a pediatrician at UCSF, I treated children with diseases whose conditions were, in many cases, still mysteries to science,” said Biohub co-founder Priscilla Chan. “What I wanted more than anything was a way to see what was happening inside their cells… AI is changing that. For the first time, we have the potential to model and predict the biology of disease in ways that can reveal what’s gone wrong and how we can develop new treatments to address it.”
AI in action: virtual immune system and new models
Biohub is also launching the Virtual Immune System, designed to model the human immune system’s complexity, offering potential for drug development, immune therapies and disease prevention. Additionally, three new AI models – VariantFormer, CryoLens and scLDM – are being released publicly, complementing existing tools for AI-driven research in biology.
By using AI to simulate biological processes digitally and accelerate fundamental discovery, Biohub wants to change drug development for the future, making it possible to develop treatments faster, personalise therapies and even foresee new therapeutic approaches.
