Biohub releases a world model of protein biology

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May 27, 2026

Biohub releases a world model of protein biology

Biohub’s open models map the protein universe and design functional binders with therapeutic-level affinity in the lab.

AI, Data, Technology

REDWOOD CITY, Calif., May 27, 2026 – Biohub today announced the release of a world model of protein biology: a scientific engine for prediction, design, and discovery that can map proteins across the tree of life, predict their structures, and design new protein binders that function in laboratory experiments.

Proteins are the machinery of life. Nearly every function of the human body depends on them. They are among the most important targets in medicine, yet designing functional, stable proteins that work as intended in the body is an immense scientific and technical challenge.

ESMC provides a foundation for modeling the sequence, structure, and function of proteins. ESMFold2 predicts the structure of proteins and biomolecular complexes with state-of-the-art accuracy and speed. Features derived from model representations capture fundamental principles of structure and function, forming a compositional grammar for protein biology.

Today, Biohub is making available to researchers everywhere an open discovery engine for protein structure prediction, design, and biological discovery built around three releases: ESMC, ESMFold2, and ESM Atlas:

The core scientific hypothesis of ESM is that training a language model across the sequences of all life will cause it to internalize the fundamental properties that govern protein biology — the rules underlying how proteins fold, interact, and function across all of life. At its foundation is ESMC , a state-of-the-art language model that represents proteins, trained on approximately 2.8 billion sequences drawn from across all of life.

ESMFold2 is the design engine built to transform ESMC’s sequence representations into atomically-resolved 3D structure of biomolecular complexes. In experiments described in a preprint posted today, researchers used ESMFold2 to design protein binders against five targets central to cancer and immunology — a computational search completed in days, rather than several months or years. The lab-validated binders exhibited high affinity, specificity, and stability — properties critical for clinical utility — and showed minimal similarity to sequences in public databases, suggesting the model is producing de novo solutions, rather than retrieving known binders.

ESM Atlas makes ESMC’s representations navigable across 6.8 billion protein sequences and 1.1 billion predicted structures — the largest application of AI to protein biology to date. It organizes proteins by relationships the model has learned, surfacing connections that existing databases have not captured, including evolutionary links between gene-editing enzymes spread across distant branches of life. Much of that biology has never been annotated. For researchers working on diseases where the biology is poorly understood, it makes uncharacterized biology searchable.

All three are freely available to the global scientific community at Biohub Platform.

“Designing the interactions between proteins is a fundamental problem in biochemistry, and critical for the design of medicines. What we’ve shown is that these models have learned such a high-fidelity world model of biology that you can design protein interfaces computationally, take them into the laboratory, and they function as predicted.”

— Alex Rives, Head of Science, Biohub

ESMFold2: A faster path from protein biology to binder design

ESMFold2 is an open, state-of-the-art structure prediction model that translates knowledge of patterns across evolution encoded in ESMC into precise, atomic-resolution 3D models of proteins and their interactions. It leads across standard protein folding benchmarks at predicting protein-protein and antibody-antigen interactions.

ESMFold2 achieves state-of-the-art accuracy in structure prediction, both for general protein-protein interactions and for the challenging and therapeutically relevant task of antibody-antigen prediction. From ESMC representations alone, ESMFold2 is more successful at predicting the true binding pose of antibody-antigen complexes than AlphaFold 3. When provided with the same evolutionary information (MSA) as AlphaFold, ESMFold2 is the strongest predictor on both benchmarks. Bottom: structure prediction models can benefit from a larger computational budget. When we let models make multiple predictions and score them based on their own confidence estimates, ESMFold2 consistently improves with more compute.

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