Synthesize Bio, a Seattle-based startup founded by leaders from Fred Hutchinson Cancer Center, announced $10 million in funding from Madrona Venture Group.
The biotech company aims to make new drug discovery faster and cheaper by using artificial intelligence to simulate the results from hypothetical lab tests. The team built what it’s calling a generative genomics foundation model (GEM-1) to predict gene expression, providing insights into how a novel drug is expected to impact cell behavior.
On Tuesday, Synthesize Bio researchers published a preprint on bioRxiv explaining the model and its performance, which the scientists said “predicts future gene expression experimental results with lab-level accuracy.”
Synthesize Bio has a 16-person team and was co-founded nearly two years ago by Fred Hutch Chief Data Officer Jeff Leek and Robert Bradley, director of the Translational Data Science Integrated Research Center at Fred Hutch.
The team trained its model on publicly available information correlating research experiments with RNA sequencing data — which captures the genes being expressed in a cell. The dataset included healthy and diseased human cells and tissue that was taken from lab work and clinical trials.
“This is the kind of infrastructure shift that redefines what’s possible in life science R&D,” Matt McIlwain, managing director at Madrona, said on LinkedIn. “With generative genomics, researchers can ask bolder questions, design smarter trials, and test hypotheses that were previously unaffordable or impossible to explore.”
In its preprint article, the Synthesize team elaborated on the problem it is trying to solve. “Many lab experiments can be conducted no faster than the speed with which cells grow or diseases develop,” the company wrote, “while clinical trials are similarly governed by the availability and recruitment of participants and essential regulations to protect patients.”
The generative capabilities of AI models, they continued, “offer the possibility of circumventing these fundamental biological constraints by computationally simulating many, or even all, limiting experimental steps.”
Synthesize joins a flurry of biotech startups and research groups developing AI-powered systems in the Seattle area, including:
- The University of Washington’s Institute for Protein Design (IPD) uses AI to build novel proteins that could be used in treating wide-ranging diseases.
- The Fred Hutch-led Cancer AI Alliance, a consortium that also includes Dana-Farber, Memorial Sloan Kettering, and Johns Hopkins with support from the Allen Institute for AI (Ai2) and Google Cloud.
- The Allen Institute’s Seattle Hub for Synthetic Hub, which is using a DNA-based technology to make a recording of what a cell experiences over time.
- Startups including Xaira Therapeutics, Archon Biosciences, Lila Biologics, Outpace Bio, A-Alpha Bio, Talus Biosciences, Potato and many others. IPD alone has spun off 10 startups.
Other authors of the preprint titled “Generative genomics accurately predicts future experimental results” are: Gregory Koytiger, Alice M. Walsh, Vaishali Marar, Kayla A. Johnson, Max Highsmith, Alexander R. Abbas, Andrew Stirn, Ariel R. Brumbaugh, Alex David, Darren Hui, Jeffrey M. Kahn, Sheng-Yong Niu, Liza J. Ray, Candace Savonen and Stein Setvik.
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