Protein Language Models: Builders & Pharma Deals

We unpack how PLMs work, notable builders, pharma deals, and outline current limitations

Chan Zuckerberg Initiative, the group behind the recent virtual cell efforts, has “acqui-hired” EvolutionaryScale’s ~50-person team, folding it into the expanding Biohub network. The move comes as CZI pivots to center nearly all its resources on AI-driven biology. EvolutionaryScale’s chief scientist, Alex Rives, will now serve as Biohub’s new head of science, succeeding Steven Quake.

EvolutionaryScale emerged in 2023 after Rives, along with Tom Sercu and Sal Candido, left Meta’s AI protein group (FAIR) during the company’s “year of efficiency” (there are, again, plans to cut 600 AI jobs after a $14.3 billion Scale AI investment and hiring spree this summer). Backed by the likes of Amazon and Nvidia, the team raised $142 million to develop large-scale generative models for protein design and became known for the ESM family of protein language models (PLMs) trained directly on amino-acid sequences.

Its flagships, ESM3 and ESM Cambrian, extended this work to fully generative modeling of protein structure and function. ESM3, trained on 2.7 billion proteins, has already been used to design molecules like the novel green fluorescent protein variant, esmGFP, said to represent roughly 500 million years of natural evolution.

CZI’s Biohub folds this hire into its broader “virtual biology” plan, setting out four scientific challenges: building an AI-based model of the cell, advancing imaging, instrumenting inflammation, and using AI to reprogram the immune system, with the Virtual Immune System as one of the flagship projects. The ES team is brought in “to help advance this initiative.” In the VIS roadmap, the molecular-interactions axis explicitly calls for protein language models that “can learn the universal grammar of immune recognition and enable the rational design of novel receptors.”

With that, let’s step back and look closer at what protein language models are, what kinds of applications companies are building them for, and where pharma is already involved.

In this article: Proteins & Language — Players & Pharma Collaborations — Sequence-Structure Gap — Challenges & Prospects