In August 2023, as part of Mark Zuckerberg’s “year of efficiency” that resulted in over 20,000 layoffs, Meta disbanded a research team of a dozen scientists who had trained a large AI language model for biology.
But Alexander Rives, who leads the research cohort known as Metta’s “protein AI team,” was undeterred by Metta’s move. He promptly formed a startup with a core group of his former Meta colleagues called EvolutionaryScale to continue his work on building large language models that, instead of generating text, images, or video, generate recipes for entirely new proteins.
This idea is essentially to make biology programmable, with potential applications ranging from drug development and cancer treatment (such as antibodies) to environmental protection techniques (for example, enzymes – which are proteins – can help break down plastic) . Researchers could specify the protein’s function and other attributes, such as its toxicity to humans, as a prompt and have the AI model return the DNA formula for making that particular protein.
Today, EvolutionaryScale, based in New York and San Francisco, announced that it has raised over $142 million in seed funding led by Nat Friedman and Daniel Gross, and Lux Capital, with participation from Amazon Web Services (AWS), NVentures (venture capital of Nvidia arm) and angel investors. The announcement adds Reeves and his former Meta team to a growing list of Meta alumni — a sort of “Meta AI mafia” — who have made waves with new startups in the space, most notably Mistral.
In addition to the funding, the company also announced that it has created ESM3, which Reeves said Wealth is a generative model for biology that has been trained on more computations than any other LLM in this space. The model, after being trained on nearly 4 billion proteins from the natural world, can simultaneously reason about DNA sequence, physical structure, and protein function—three fundamental aspects of protein biology and biochemistry. And in a new paper, EvolutionaryScale showed how it applied ESM3 to generate an entirely new fluorescent protein – a type of protein first isolated in glowing jellyfish – that would take millions of years of evolution to create in nature.
The AI model, he explained, can process the three-dimensional structure of proteins as a language—like an alphabet of different characters—which can then be prompted like other models, including ChatGPT. But in this case, the protein “grammar” allows the model to be prompted with any combination of the protein’s sequence, structure, and function. “We see that the model is able to find very creative solutions to these prompts,” he said.
To train such a large-scale model requires expertise in both biology and machine learning and massive amounts of computing power – which explains the astonishing early fundraising. “They require a large amount of computation to build and train, similar to other boundary modeling efforts in AI,” Reeves said. The fundraising, he said, “really reflects the resources we need to do this.”
EvolutionaryScale is far from the only company targeting the potential of AI-powered generative biology or even pursuing a dedicated LLM. InstaDeep, a London-based company that was acquired in 2023 by BioNTech, best known for helping to create Pfizer’s COVID vaccine, has created an LLM in genomics, although not as large as the one EvolutionaryScale is working on . Profluent, a San Francisco-based AI biotech startup, is also focusing on developing an LLM to design new proteins. And Google DeepMind’s AlphaFold is a model that predicts protein structure using a generative AI model.
Friedman, who led the funding round at EvolutionaryScale with his investment partner Gross, said Rives and the other Meta alumni are a “dream team.” (Gross recently teamed up with ex-OpenAI Ilya Sutskever and Daniel Levy to launch a new startup, Safe Superintelligence.)
“This was clearly the team that had invented protein language modeling and had all the capacity to keep scaling it up,” Friedman said. “Alex thinks very big. He wants to build a complete multimodal model that captures all the complexity of biology. I was looking for someone who had that ambition and vision, scale of thinking and experience to do it.
Rives said ESM3 will have an immediate impact on scientific research, with scientists being able to use open versions of the model for free. The company will also offer a commercial version for pharmaceutical companies to use in drug discovery and development. This is similar to the model pursued by Google DeepMind, with a version of AlphaFold available to researchers for free, but with a separate company, Isomorphic Labs, working on partnerships with pharmaceutical companies.
As for Meta, Rives said he wasn’t too surprised when the company disbanded his team.
“Meta is not a biotech company,” he said. Although Meta’s open research culture made it an “incredible” place to do the work, he added, “we were getting to the point where we really wanted to move to the next level of scaling these models. I think building a new company was really the right place for that.”
It’s an “incredibly talented group of alumni that came out of” Meta, Friedman said. “They’ve hired amazing people – and I think the EvolutionaryScale team is very grateful to Meta for incubating their efforts.”
