Over the past few years, AI has made incredible strides in biology. State-of-the-art models can now predict protein structures and even design potential drug candidates from scratch. Over time, AI could become powerful enough to create new medicines for a wide variety of diseases. But realizing this vision requires generating orders of magnitude more data than we currently have and rearchitecting how we collect and structure that data to make it model-ready.
BigHat Biosciences is building the enabling infrastructure, data generation engine, and AI models to make this possible, and we are proud to announce that we co-led the company’s $75 million Series C financing.
Advancing AI in biology requires testing a model’s predictions and learning from the results, but the slow iteration speed and complexity of physical experiments constrain the pace of improvement. In math and coding, models have advanced so quickly because outputs can be tested almost instantly—answers can be checked, and code can be compiled and verified. Those results provide rapid feedback for a process called reinforcement learning, which has helped drive AI’s skyrocketing capabilities.
Creating this feedback loop in biology is much harder. When a model designs a never-before-seen molecule, it needs to be made in a physical lab and tested across dozens of distinct properties. Those results then need to be fed back into the model. The end goal is teaching AI to create viable medicines that do more than bind to the right target. They also need to be stable, manufacturable, and functionally safe and effective in the body.
BigHat built its technology stack to improve this feedback loop. Its “lab-in-the-loop” platform makes and tests thousands of novel proteins every week, generating proprietary real-world data to improve its models and the next round of protein designs.
BigHat’s lab is also highly automated, but automation alone is not enough for the speed that AI requires. The company runs on a technology called cell-free protein synthesis, which enables it to produce novel proteins without live cells. Traditional methods rely on cells as miniature protein factories and are therefore fundamentally limited by how fast cells can grow. Removing the need to grow cells dramatically increases speed, but making this process reliable, reproducible, and translatable at scale is challenging. Since the company’s founding in 2019, BigHat has optimized and industrialized every step of this process.
BigHat leverages its powerful combination of AI expertise, proprietary data, and experimental infrastructure to both develop its own models and partner with frontier labs, startups, and biopharma. Because BigHat is AI-native, the team deeply understands what kinds of experimental data are needed to develop cutting-edge models—and how to collect that data effectively. This “taste” stands out and has helped BigHat partner with leading pharma companies like Eli Lilly, Merck, Amgen, AbbVie, and J&J. BigHat is also working with multiple frontier labs and is rapidly scaling its lab capacity to keep up with demand.
To demonstrate its capabilities, BigHat recently reached a major milestone: dosing its first patient with a fully AI-designed medicine.
This is one of the first AI-designed drug candidates to reach the clinic, demonstrating BigHat’s ability to carry a program from computational design through real-world drug development. The program is an early example of how AI could bring a new generation of treatments to humanity.
BigHat is led by co-founder and CEO Peyton Greenside, who founded the company after earning her PhD in biomedical informatics from Stanford. Peyton recognized early on that progress in AI-driven biology would require high-quality data to train and improve models. We have known Peyton for several years and have been incredibly impressed by her vision and dedication to deploying AI to improve human health.
We are thrilled to support Peyton and the BigHat team on their quest to bring about a world where AI will help discover new medicines that improve human health. The company is growing quickly and hiring, so please reach out if you’d like to join its mission!
The BigHat Bio Team
