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ESM-1v

By Meta AI (FAIR) →

Zero-shot prediction of protein variant effects with language models

ESM-1v is an ensemble of five 650M-parameter protein language models trained to predict the functional consequences of single amino acid mutations across thousands of diverse proteins without training on experimental labels.

State-of-the-art zero-shot variant effect prediction across 41 deep mutational scanning datasetsEnsemble of five distinct models trained on UniRef90 with different random seedsCaptures epistatic and structural constraints directly from evolutionary sequencesStandard benchmark baseline in computational biology for mutation scoring
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Overview

ESM-1v is an ensemble of five 650M-parameter protein language models trained to predict the functional consequences of single amino acid mutations across thousands of diverse proteins without training on experimental labels.

Information checked against an official source; not a hands-on test. Source · Last reviewed: 20/09/2026, 11:10:15

Key Features

  • State-of-the-art zero-shot variant effect prediction across 41 deep mutational scanning datasets
  • Ensemble of five distinct models trained on UniRef90 with different random seeds
  • Captures epistatic and structural constraints directly from evolutionary sequences
  • Standard benchmark baseline in computational biology for mutation scoring
Interactive 3D Structure

ESM-1v Predicted Complex

Streams real 3D atomic coordinates from RCSB Protein Data Bank
⇄ Drag to rotate · Scroll to zoom
pLDDT / B-Factor:
>90 Very high 70-90 Confident 50-70 Low <50 Very low

Academic Context & Research Evidence

Biological & Workflow Fit

Biological Application
Predicting viral escape, disease-associated mutations, and enzyme thermostability
Research Workflow
Wild-type sequence + candidate mutations -> compute log-likelihood ratio scores
Compute & Hardware
1x GPU (>= 16GB VRAM) for batch scoring
Licensing & Academic Use
MIT License
Documented Evidence
View validation publication / source ↗

Cite this Tool

Use this citation format when referencing ESM-1v in scientific publications and benchmark papers.

@software{esm_1v_2026,
  title = {{ESM-1v}},
  author = {{Meta AI (FAIR)}},
  year = {2026},
  url = {https://github.com/facebookresearch/esm},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations streamed from Europe PMC and PubMed for ESM-1v.

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Technical / Product Information

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeAI Model
Access modeOpen Source
AI roleVariant Effect Prediction
Input dataNot recorded
Output dataNot recorded
Licence conditionsMIT License
Commercial eligibilityPermissive open source release
Compute requirements1x GPU (>= 16GB VRAM) for batch scoring
ValidationNot recorded
TypeMasked protein language model ensemble
Intended useNot recorded
CompatibilityNot recorded
ManufacturerMeta AI
Biological applicationPredicting viral escape, disease-associated mutations, and enzyme thermostability
Research workflowWild-type sequence + candidate mutations -> compute log-likelihood ratio scores
Evidence levelPeer-reviewed research (NeurIPS 2021)
Integration evidencehttps://github.com/facebookresearch/esm
Laboratory handoffGuides directed evolution and deep mutational scanning library design
AvailabilityAvailable on GitHub and PyPI
Price / accessFree Open Source

Research fit & compatibility

No software–hardware integration has been verified for this entry yet. Explore documented research workflows.

Gallery & resources

No licensed product photograph recorded for ESM-1v yet. Visit the official resource below for manufacturer imagery.

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FAQ

Where is this product available?

Available on GitHub and PyPI

How is pricing handled?

Prices reflect the source at its last check. Confirm current pricing and regional availability on the official site.

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Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating ESM-1v.

5.0
★★★★★Based on 0 researcher evaluations
Biological Accuracy
4.8/5
Ease of Installation
4.3/5
Documentation & Code
4.6/5