ESM3 - Biohub Platform

ESM3 is a multimodal generative protein language model that jointly models sequence, structure, and function. It enables controllable generation of novel proteins by conditioning on any combination of these modalities.

Hugging Face

GitHub

Paper

API Reference

ESM3-open is released under the MIT License. See the Variants tab for availability and access methods for all ESM3 variants.

Get Started

Get started using ESM3 with our Quickstart and Tutorials.

Quickstart Guide

Install the esm Python package

pip install esm

Create an API key

Generate an API key from your Biohub account. This API key manages your access to credits and tokens, and the term API key/token is often used interchangeably within documentation.

Connect to the Biohub Platform API

Call the ESM3 inference client with the selected model of choice and replace <your API token> with your token name.

from esm.sdk.forge import ESM3ForgeInferenceClient

client = ESM3ForgeInferenceClient(model="esm3-open-2024-03", url="https://biohub.ai", token="<your API token>")

Run your inference

Now you are ready to use your model. For examples of generating novel proteins with ESM3, check out our Tutorials.

Model Tutorials

Understanding the ESMProtein class

Get familiar with how ESM3 represents proteins.

Generating proteins with ESM3

Learn how to scaffold a functional motif, edit secondary structure, and guide design using solvent exposure.

Designing a novel GFP with ESM3

Walk through the exact prompting strategy used to design a novel fluorescent protein with no close natural relatives.

Guided generation with ESM3

Add scoring functions into the generation process, such as structural quality, sequence constraints, or other properties.

Model Details

For additional information, see the Hugging Face link.

Model Card

Cite this Model

Simulating 500 million years of evolution with a language model

@article{esm3_2025,
  title={Simulating 500 million years of evolution with a language model},
  author={Hayes, Thomas and Rao, Roshan and Akin, Halil and Sofroniew, Nicholas J. and Oktay, Deniz and Lin, Zeming and Verkuil, Robert and Tran, Vincent Q. and Deaton, Jonathan and Wiggert, Marius and Badkundri, Rohil and Shafkat, Irhum and Gong, Jun and Derry, Alexander and Molina, Raul S. and Thomas, Neil and Khan, Yousuf A. and Mishra, Chetan and Kim, Carolyn and Bartie, Liam J. and Nemeth, Matthew and Hsu, Patrick D. and Sercu, Tom and Candido, Salvatore and Rives, Alexander},
  journal={Science},
  volume={387},
  number={6736},
  pages={850--858},
  year={2025},
  doi={10.1126/science.ads0018}
}

Overview
Details
Variants
Usage


Version	2024-03
Architecture	Transformer
Supported Modalities	Sequence, structure, function
Training Data	3,000+ sequences and 700,000+ unique training tokens

Intended Use

ESM3 is designed for prompt-driven generation of sequences and structures based on inputs of motifs, partial coordinates, secondary structure (SS) constraints, or function keywords.

Limitations & Risks

Novel sequence generation can lead to designs with hazardous properties. Model proposals may not be physically realizable; pLDDT/pTM are helpful but imperfect. Not intended for clinical or therapeutic applications without further validation.ESM3-open is released under the MIT License. See the Variants tab for availability and access methods for all ESM3 variants.


Parameters	1.4, 7, and 98 billion parameters for ESM3 Small, Medium, and Large respectively.
Transformer layers	48, 96, 216 layers for ESM3 Small, Medium, and Large respectively.
Type of model architecture	Pre-LN transformer architecture with rotary embeddings and SwiGLU non-linearities. The first transformer block includes an SE(3)-invariant Geometric Attention layer which conditions on backbone atomic coordinates and encodes geometric relationships.
Training FLOPs	10²¹, 10²², and 10²⁴ for ESM3 Small, Medium, and Large respectively.
Training datasets	ESM3 was trained on protein sequences from PDB, UniRef, OAS, metagenomic sequences from MGnify and JGI, and synthetic data from AFDB, ESM Atlas (inverse folded), and ESM Atlas (inverse folded). Sequences, structures, and annotations were part of the training set.

To obtain API access to ESM3 variants aside from ESM3-open, fill out this form with your project details and desired model.Flagship ModelsMost users will be interested in using one of these models.

Model	Model Size	Max Context Length	Release Date	Availability
esm3-open-2024-03	1.4B	2048	2024-03	Biohub Platform API, GitHub, and Hugging Face
esm3-large-2024-03	98B	2048	2024-03	Biohub Platform API
esm3-medium-2024-08	7B	2048	2024-08	Biohub Platform API
esm3-small-2024-08	1.4B	2048	2024-08	Biohub Platform API

Published ModelsThese models were used to generate all of the results in the ESM3 paper and are provided to facilitate reproducibility.

Model	Model Size	Max Context Length	Release Date	Availability
esm3-large-2024-03	98B	2048	2024-03	Biohub Platform API
esm3-medium-2024-03	7B	2048	2024-03	Biohub Platform API
esm3-small-2024-03	1.4B	2048	2024-03	Biohub Platform API


Primary Use Case	Protein representation learning and embeddings, generating protein embeddings that can be fine-tuned for various downstream prediction tasks including functional annotation, mutational effect analysis, and the design of novel proteins and peptides, and predicting the functional impact of mutations and amino acid substitutions on protein function.
Supported Input Modalities	Protein sequences
Access	Access ESM3-open through Biohub or Hugging Face. To obtain API access to ESM3 variants aside from ESM3-open, fill out this form with your project details and desired model.
License	ESM3-open is released under the MIT License. See the Variants tab for availability and access methods for all ESM3 variants.
Not recommended for	ESM3 can exhibit poor performance when modifying sequences on which it is highly confident. Not recommended for clinical diagnosis or treatment recommendations. Computational metrics do not replace wet-lab validation. Treat model outputs as machine-generated hypotheses that require further experimental validation, not as established biological facts.

Hugging Face

GitHub

Paper

API Reference

​Get Started

​Quickstart Guide

​Model Tutorials

Understanding the ESMProtein class

Generating proteins with ESM3

Designing a novel GFP with ESM3

Guided generation with ESM3

​Model Details

​Model Card

​Intended Use

​Limitations & Risks

Get Started

Quickstart Guide

Model Tutorials

Model Details

Model Card

Intended Use

Limitations & Risks