ESMC and ESMFold2 are protein biology world models released by Biohub on May 27, 2026. Similar to how GPT learns the grammar and semantics of human language from a massive amount of text, ESMC is a 6 billion parameter foundational language model that has been pre-trained on approximately 2.8 billion protein sequences collected from all life forms on Earth, thereby acquiring the protein design grammar that evolution has accumulated over billions of years. ESMFold2 combines this ESMC embedding with a looped transformer and diffusion-based structure prediction architecture, creating an engine that predicts all-atom 3D structures from a single amino acid sequence and further designs therapeutic protein binders. The AlphaFold family, which was previously the standard for protein structure prediction, heavily relies on multiple sequence alignment (MSA). While MSA-based approaches are powerful for evolutionarily conserved proteins, they have structural limitations in that the alignment signal is lost in rapidly mutating sequences, such as antibodies, leading to a significant drop in accuracy. ESMC-ESMFold2 adopts the so-called "The Bitter Lesson" philosophy, betting on the scaling law that training a sufficiently large and diverse BERT-based transformer without MSA will outperform specialized architectures. As a result, it achieves higher accuracy than AlphaFold 3 in antibody-antigen binding prediction and also outperforms competing models such as Chai-1 and Boltz-1. In single-sequence mode, it provides approximately a 10-fold increase in inference speed compared to MSA-based methods, making it suitable for large-scale screening. From the perspective of a biotechnology researcher, the most noteworthy aspect is its ability to design therapeutic binders that have been validated in the laboratory. The Biohub team designed binders with nanomolar affinity for five cancer and immune targets (EGFR, PDGFRβ, PD-L1, CTLA-4, and CD45) with hit rates of 36-88% for compact minibinders and 15-29% for antibody-derived formats, and the PD-L1 binder has even demonstrated the ability to restore T-cell signaling in vivo. This reduces the target binder design cycle from months or years to just a few days. Furthermore, through ESM Atlas, it is possible to explore 6.8 billion sequences and 1.1 billion predicted structures, and to map the function of unannotated proteins using approximately 16,000 interpretable features based on a sparse autoencoder (SAE). The model and code are fully open-source under the MIT license, allowing both academic and industrial researchers to run it directly on their local GPUs or access it through the Biohub platform API.

Protein Biology World Model — A platform for understanding and designing proteins at an evolutionary scale.

Design of Antibody Therapeutic Candidate Binders: Utilize ESMFold2's de novo binder design protocol to generate minibinders against immune checkpoint targets such as PD-L1 and CTLA-4. Achieve a hit rate of 36-88% and nanomolar affinity based on compact minibinder criteria. After design, visualize the 3D binding structure with PyMOL or ChimeraX to determine the priority for experimental validation. This significantly reduces the time to reach therapeutic candidates by shortening the design cycle from several months to 3-5 days.