EvolutionaryScale, an AI Biotech Startup Founded by Former Meta Researchers, Secures $40 Million in Funding

Biological LLM

EvolutionaryScale, a new AI biotech startup founded by former researchers from Meta, has recently raised $40 million in funding to develop advanced language models for biology. Led by Alexander Rives, who spearheaded Meta AI’s protein-folding team, EvolutionaryScale aims to leverage artificial intelligence to revolutionize various aspects of biotechnology, from cancer treatment to environmental remediation.

The founding team of EvolutionaryScale comprises eight members who previously worked together at Meta, where they developed a transformers-based model inspired by OpenAI’s GPT-4 and Google’s Bard. However, their model was specifically trained on data related to protein molecules, enabling them to predict the structures of unknown proteins. This breakthrough technology has led to the creation of a vast database with over 700 million potential 3D structures—a valuable resource for drug development, pollution cleanup, and alternative industrial manufacturing.

In June, EvolutionaryScale began seeking seed financing to scale up its research efforts and expand the size of its AI model. The funding round, which raised approximately $40 million, was led by Lux Capital. Notably, the startup achieved a valuation of $200 million, and prominent AI investors Nat Friedman and Daniel Gross participated in the round.

The ability to accurately predict the structure of proteins is critical for understanding their functions and designing targeted drugs. DeepMind’s AlphaFold made significant advancements in this field by releasing an AI system for predicting protein structures. However, EvolutionaryScale’s model, as described in a paper published in the journal Science, demonstrated predictions that were 60 times faster than AlphaFold, albeit slightly less accurate on average.

While AI has shown incremental improvements in drug development efficiency, a transformative moment akin to the eureka moment in text-based generative AI has yet to occur in the field of biology. Nonetheless, EvolutionaryScale’s retrieval augmented generation technology holds tremendous potential to drive innovation and reshape the landscape of biotech research.

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The funding obtained by EvolutionaryScale will be instrumental in scaling up their AI model and investing in substantial computing power. The startup anticipates spending $38 million in its first year, with costs projected to increase to $161 million in year two and $278 million in year three. Throughout this period, EvolutionaryScale aims to continually refine its model and explore opportunities beyond protein structure prediction, such as integrating other biological data like DNA sequences and gene expression.

One of the key challenges for EvolutionaryScale is establishing its competitive advantage over DeepMind’s AlphaFold. Other companies, including Inceptive, are also emerging to tackle similar goals using large language models for designing RNA-based drug therapies.

Despite the long road ahead, EvolutionaryScale remains committed to its vision of enabling breakthroughs in AI for biology. By leveraging cutting-edge technology and interdisciplinary research, the startup aims to contribute to the development of programmable cells to combat diseases, molecular machines for environmental cleanup, and more.

As the AI biotech sector continues to attract significant investments, it is clear that transformative advancements in this field have the potential to revolutionize healthcare, environmental sustainability, and various other industries.

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About Author

Teacher, programmer, AI advocate, fan of One Piece and pretends to know how to cook. Michael graduated Computer Science and in the years 2019 and 2020 he was involved in several projects coordinated by the municipal education department, where the focus was to introduce students from the public network to the world of programming and robotics. Today he is a writer at Wicked Sciences, but says that his heart will always belong to Python.