ESM-2

Overview

ESM-2 (Evolutionary Scale Modeling) is a family of protein language models developed by Meta's Fundamental AI Research (FAIR) team, ranging from 8M to 3B parameters, that learn structural and functional representations directly from protein sequence. ESM-2 is the base model for ESMFold's single-sequence structure prediction and underpins a wide range of genomics-adjacent variant-effect-on-protein and protein-design workflows. Its source code is confirmed MIT licensed (the ESM Metagenomic Atlas *data*, a separate artefact, carries a separate CC-BY-4.0 licence not applicable to the model weights/code catalogued here).

**Registry note on `model_card_uri`**: this points to the model's standard Hugging Face model card, not a GA4GH Genomic AI Model Card v0.6.0-formatted document. No model has yet published a card in the GA4GH v0.6.0 format, since it is a newly drafted specification. ESM-2 is protein-sequence-native rather than DNA-native; it is catalogued here because protein-level variant-effect modelling is a common downstream step in genomic AI pipelines, per PRD.md Section 4.1's task-relevance test rather than a DNA-specific one.

Details

Licence

MIT

Version

esm2_t36_3B_UR50D

Category

Protein structure and function

Homepage

https://huggingface.co/facebook/esm2_t36_3B_UR50D

Repository

https://github.com/facebookresearch/esm

Maintainers

Susheel Varma (@susheel, GA4GH AI Workstream / Sage Bionetworks)

Keywords

proteinprotein-language-modelstructure-predictionesmfold

Safety classification

No safety classification has been submitted for this entry.

Model Card

View the full GA4GH Genomic AI Model Card ↗

Hosting

huggingface

Access

https://huggingface.co/facebook/esm2_t36_3B_UR50D

Mirrored data last synced from the Model Card at 2026-09-04T11:05:00.000Z. See the linked card for the authoritative, live version.

Provenance

Created

2026-09-04T11:05:00.000Z

Updated

2026-09-04T11:05:00.000Z

Last verified

2026-09-04T11:05:00.000Z