But Alexander Rives, who led the research cohort known as Meta’s “AI protein team,” was not deterred by Meta’s move. He immediately spun up a startup with a core group of his former Meta colleagues, called EvolutionaryScale, to continue their work building large language models that, instead of generating text, images, or video, generate recipes for entirely new proteins.

This idea is to essentially 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 degrade plastic). Researchers would be able to specify the function of the protein and other attributes, such as its toxicity to humans, as a prompt and have the AI model return the DNA formula for manufacturing exactly that protein.

Today, EvolutionaryScale, based in New York and San Francisco, announced it has raised over $142 million in seed funding, led by Nat Friedman and Daniel Gross, and Lux Capital, with participation from Amazon, NVentures (Nvidia’s venture capital arm), and angel investors. The announcement adds Rives and his ex-Meta team to a growing list of Meta alumni—a “Meta AI mafia” of sorts—that have made waves with new startups in the space, most notably Mistral.

In addition to the funding, the company also announced that it had created ESM3, which Rives told Fortune is a generative model for biology that has been trained on more computation than any other LLM in that space. The model, having been trained on nearly 4 billion proteins from the natural world, can simultaneously reason over the DNA sequence, physical structure, and function of proteins—three fundamental aspects of proteins’ 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, in nature, would take millions of years of evolution to create.  

The AI model, he explained, can process the three-dimensional structure of proteins as a language—as an alphabet of different characters—that can then be prompted like other models including ChatGPT. But in this case, the “grammar” of proteins allows the model to be prompted with any combination of the sequence, structure, and function of a protein. “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 jaw-dropping early fund raise. “They require a great deal of compute to build and train, similar to other frontier modeling efforts across AI,” Rives said. The fund raise, he said, “really reflects the resources that we need to do that.”  

EvolutionaryScale is far from the only company homing in on the potential for generative AI-powered biology or even pursuing LLMs specifically. InstaDeep, a London-based company that was acquired in 2023 by BioNTech, best known for helping to create the Pfizer COVID vaccine, has created an LLM for genomics, although it is not as large as the one EvolutionaryScale is working on. Profluent, a San Francisco–based AI-enabled biotech startup, is also focused on developing LLMs to design new proteins. And Google DeepMind’s AlphaFold is a model that predicts the structure of proteins using a generative AI model.

Friedman, who led the funding round in EvolutionaryScale with his investing partner Gross, said Rives and the other Meta alumni are a “dream team.” (Gross recently teamed up with ex-OpenAI’s 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 continue to scale this up,” Friedman said. “Alex thinks very big. He wants to build a full multimodal model that captures all the complexity of biology. I had been looking for someone who had that ambition and vision and scale of thinking and the expertise to pull it off.” 

Rives said ESM3 will have an immediate impact on scientific research, with academics 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 Google DeepMind has pursued, with a version of AlphaFold available to researchers for free, but with a separate spinout company, Isomorphic Labs, working on partnerships with pharmaceutical companies.

As for Meta, Rives said he was not terribly surprised when the company disbanded his team. 

“Meta is not a biotechnology company,” he said. While Meta’s open research culture made it an “incredible” place to do the work, he added, “we were reaching the point where we really wanted to go to the next level of scaling these models. I think building a new company was really the right place to do that.” 

It’s an “unbelievably talented alumni group that’s come out of” Meta, Friedman said. “They hired some incredible people—and I think the EvolutionaryScale team is very grateful to Meta for incubating their efforts.”