The UW Medicine Institute for Protein Design (IPD) has announced a giant grant of computing power from the Jen-Hsun and Lori Huang Foundation, supercharging its research into new protein discovery.
“We have made more rapid progress on methods development than any time in my career,” said David Baker, director of the IPD and a 2024 Nobel laureate in chemistry. “I mean, it’s just been totally mind-boggling.”
In June, the IPD began accessing Nvidia graphics processing units, or GPUs, through the cloud computing company CoreWeave. The grant from the foundation, which was co-founded by Nvidia CEO Jensen Huang and his wife, Lori Huang, totals nearly 7 million GPU hours. The allocation is expected to support the institute for a year.
The IPD’s work has long been rooted in computation. In 2003, Baker and fellow University of Washington researchers used their Rosetta software to design a protein with a structure unlike any known in nature. In recent years, the institute has embraced deep learning, releasing tools such as RoseTTAFold for predicting protein structures and RFdiffusion for designing new proteins.
With this technology, the institute designs previously unimagined proteins with potential for treating intractable diseases and tackling environmental challenges such as plastic waste and toxic pollutants. The tools are open source and available for public use.
Running them takes vast computing power, though, and until now the institute has lacked the resources to pay for outside cloud services. It has relied instead on its own computer cluster, which works but is limited. Baker said IPD scientists meet every Tuesday at 5 p.m. to decide what to test over the coming week, keeping capacity in mind.
To advance their protein research, the team experiments with different architectures for the AI models and tweaks the datasets used for training, sometimes expanding them or adding synthetic data. With compute restrictions, however, researchers historically have made multiple changes at once rather than testing each alteration individually, a more systematic approach that yields cleaner results.
Despite the hurdles, the IPD has produced a steady stream of breakthroughs with its models and protein designs, publishing regularly in leading scientific journals such as Nature and Science.
Google DeepMind is pursuing similar AI-driven protein research. Baker said he knew the tech giant’s team had much more computing power than the IPD, but he didn’t fully appreciate what that could unlock.
That capacity “spawns more ideas because you know you can test them,” Baker said. “So it just creates this incredibly fertile, creative environment.”
Jensen Huang, the foundation’s co-president, said in a statement that the extra computing power allows the UW researchers to train AI “on tens of millions of molecular structures, learning from virtual experiments, and finding novel protein designs that have eluded nature.”
Huang predicted that many of the most important future medicines “will come from proteins that have never existed.”
Baker said the IPD still holds its Tuesday meetings, but the new compute capabilities — which he called “transformational” — open up more options.
“The team is iterating fast,” Baker said. “We get feedback on all these different computational architecture explorations, and there’s just so many different things you can try.”