A relatively new startup called EvolutionaryScale has secured a huge tranche of money to build artificial intelligence models that can generate new proteins for scientific research.
EvolutionaryScale said Tuesday it raised $142 million in a seed round led by former GitHub CEO Nate Friedman, Daniel Gross and Lux ​​Capital, with participation from Amazon and NVentures, the enterprise arm of Nvidia. The startup also released ESM3, an artificial intelligence 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 artificial intelligence is a tool for engineering from first principles the way we design structures, machines and microchips and write computer programs,” said Alexander Rives, co-founder and chief scientist of EvolutionaryScale. declaration.
Rives, along with Tom Sercu and Sal Candido, began developing generative AI models for protein exploration at Meta’s AI research lab, FAIR, in 2019. After their team was disbanded, Rives, Sercu, and Candido left Meta to to continue building he began.
Protein characterization can reveal disease mechanisms, including ways to slow or reverse it creating proteins can lead to entirely new classes of drugs, tools, and therapeutics. However, the current process of designing proteins in the laboratory is expensive, both in terms of computational and human resources.
Designing a protein means coming up with a structure that could believably perform a task inside a body or product and then find the protein sequence—the sequence of amino acids that make up the protein—is likely to “fold” into a structure. Proteins must fold correctly into three-dimensional shapes in order to perform their intended function.
Trained on a dataset of 2.78 billion proteins, ESM3 can “reason out” protein sequence, structure and function, Rives says, allowing the model to generate new proteins — à la Google DeepMind’s AlphaFold. EvolutionaryScale makes the full 98 billion parameter model available for non-commercial use through its cloud-based development platform Forge, and also releases a smaller version of the model for offline use.
EvolutionaryScale claims to have used ESM3 to create a new variant of green fluorescent protein (GFP), which is responsible for the glow of jellyfish and the luminescent colors in corals. A preprint paper on the company’s website describes its work in detail.
“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.
Of course, EvolutionaryScale is not a charity. The company, which employs about 20 people, told TechCrunch it plans to make money through a combination of partnerships, usage fees and revenue sharing. For example, EvolutionaryScale can work with pharmaceutical companies to integrate ESM3 into their workflows or share revenue with researchers for breakthrough discoveries commercialized using ESM3.
To that end, EvolutionaryScale says it will soon bring ESM3 and its derivatives to select AWS customers through the cloud provider’s SageMaker AI development platform, the Bedrock AI platform, and the HealthOmics service. ESM3 will also be available to select customers using Nvidia NIM microservices supported by the Nvidia Enterprise Software License.
EvolutionaryScale says both AWS and Nvidia customers will be able to fine-tune ESM3 with their own data.
It may take some time for EvolutionaryScale to turn a profit. In the company’s pitch deck, a copy of which Forbes was able to obtain last August, EvolutionaryScale repeatedly stressed that it could take a decade before generative AI models help design therapies. The firm will also have to fend off competition from DeepMind spin-off Isomorphic Labs, which already has deals with big 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 to create a universal AI model for biotech applications.
“The incredible pace of new advances in AI is driven by ever-larger models, ever-larger datasets and increasing computing power,” said a spokesperson for EvolutionaryScale. “The same is true in biology. In research over the past five years, the ESM team has explored scaling in biology. We find that as language models scale, they develop an understanding of fundamental principles of biology and discover biological structure and function.
This all sounds very ambitious to this reporter, but having deep-pocketed investors certainly helps.