A relatively new startup called EvolutionaryScale has secured a huge amount of funding to build AI models that can generate new proteins for scientific research.
EvolutionaryScale said Tuesday it has raised $142 million in a seed round led by ex-GitHub CEO Nat Friedman, Daniel Gross and Lux Capital, with participation from Amazon and NVentures, Nvidia’s corporate venture arm. The startup also released ESM3, an AI model it describes as a “frontier model” for biology – one that can create proteins for use in drug discovery and materials science.
“ESM3 takes a step toward a future of biology where AI is a tool to engineer from first principles, the way we engineer structures, machines and microchips and write computer programs,” said Alexander Rives, co-founder and chief scientist of EvolutionaryScale. a statement.
Rives, along with Tom Secru and Sal Candido, began developing generative AI models to explore proteins in 2019 while working at Meta’s AI research lab, FAIR. After their team disbanded, Rives, Secru, and Candido left Meta to continue building on the work they had done. had started.
Characterizing proteins can reveal the mechanisms of a disease, including ways to slow or reverse it to create proteins could lead to entirely new classes of drugs, devices and therapies. But the current process for designing proteins in the laboratory is expensive, both from a computational and human perspective.
Designing a protein means coming up with a structure that can do that plausible perform a task in the body or in a product and then find a protein sequence – the sequence of amino acids that make up a protein – that is likely to ‘fold’ into the structure. Proteins must fold properly into three-dimensional shapes to perform their intended function.
Trained on a dataset of 2.78 billion proteins, ESM3 can “reason” about the sequence, structure and function of proteins, Rives says, allowing the model to generate new proteins – à la Google DeepMind’s AlphaFold. EvolutionaryScale is making the full 98 billion parameter model available for non-commercial use through its cloud developer platform Forge and releasing a smaller version of the model for offline use.
EvolutionaryScale claims it has used ESM3 to generate a new variant of green fluorescent protein (GFP), which is responsible for glowing jellyfish and luminescent colors in coral. A pre-print article on the company’s website describes his work.
“We’ve been working on this for a long time and we’re excited to share it with the scientific community and see what they do with it,” Rives said.
EvolutionaryScale is obviously not a charity. The company, which employs about 20 people, told TechCrunch that it plans to make money through a combination of partnerships, usage fees and revenue sharing. For example, EvolutionaryScale could partner with pharmaceutical companies to integrate ESM3 into their workflows, or share revenue with researchers for breakthrough discoveries brought to market using ESM3.
To that end, EvolutionaryScale says it will soon offer ESM3 and its derivatives to select AWS customers through the cloud provider’s SageMaker AI development platform, Bedrock AI platform, and HealthOmics service. ESM3 will also be available to select customers using NVIDIA’s NIM microservices, supported by an Nvidia enterprise software license.
EvolutionaryScale says both AWS and Nvidia customers can fine-tune ESM3 using their own data.
It may take a while for EvolutionaryScale to turn a profit. In the company’s pitch deck, a copy of which Forbes managed to obtain last August, EvolutionaryScale repeatedly emphasized that it could take a decade before generative AI models help design therapies. The company will also have to fend off competition from DeepMind’s spin-off, Isomorphic Labs, which already has contracts with major pharmaceutical companies, as well as Insitro, publicly traded Recursion and Inceptive.
EvolutionaryScale’s big bet is scaling its model training to include data beyond proteins and create a general AI model for biotech applications.
“The incredible pace of new AI developments is driven by ever-larger models, ever-larger data sets and increasing computing power,” said an EvolutionaryScale spokesperson. “The same goes for biology. In research over the past five years, the ESM team has explored scaling in biology. We find that as language models scale up, they develop insights into the underlying principles of biology and discover biological structure and function.”
That all sounds wildly ambitious to this reporter, but having investors with deep pockets will certainly help.