Esm

ESM: Evolutionary Scale Modeling

Overview

ESM provides protein language models for understanding, generating, and designing proteins. Use this skill for current EvolutionaryScale/Biohub workflows: ESM3 for generative design, ESMC for representation learning and embeddings, hosted Forge/Biohub inference, and ESMFold2 all-atom structure prediction.

Core Capabilities

1. Protein Sequence Generation with ESM3

Generate novel protein sequences with desired properties using multimodal generative modeling.

When to use:

• Designing proteins with specific functional properties
• Completing partial protein sequences
• Generating variants of existing proteins
• Creating proteins with desired structural characteristics

Basic usage:

from esm.models.esm3 import ESM3
from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig

# Load local open weights after accepting the license on Hugging Face.
model: ESM3InferenceClient = ESM3.from_pretrained("esm3-open").to("cuda")

# Create protein prompt
protein = ESMProtein(sequence="MPRT___KEND")  # '_' represents masked positions

# Generate completion
protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8))
print(protein.sequence)

For remote/cloud usage via Forge API:

import os
import esm
from esm.sdk.api import ESMProtein, GenerationConfig

# Same interface as local ESM3; token from ESM_API_KEY (see Authentication)
model = esm.sdk.client("esm3-medium-2024-08", token=os.environ["ESM_API_KEY"])

# Generate
protein = model.generate(protein, GenerationConfig(track="sequence", num_steps=8))

See references/esm3-api.md for detailed ESM3 model specifications, advanced generation configurations, and multimodal prompting examples.

2. Structure Prediction and Inverse Folding

Use ESM3's structure track for structure prediction from sequence or inverse folding (sequence design from structure).

Structure prediction:

from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig

# Predict structure from sequence
protein = ESMProtein(sequence="MPRTKEINDAGLIVHSP...")
protein_with_structure = model.generate(
    protein,
    GenerationConfig(track="structure", num_steps=protein.sequence.count("_"))
)

# Access predicted structure
coordinates = protein_with_structure.coordinates  # 3D coordinates
pdb_string = protein_with_structure.to_pdb()

Inverse folding (sequence from structure):

# Design sequence for a target structure
protein_with_structure = ESMProtein.from_pdb("target_structure.pdb")
protein_with_structure.sequence = None  # Remove sequence

# Generate sequence that folds to this structure
designed_protein = model.generate(
    protein_with_structure,
    GenerationConfig(track="sequence", num_steps=50, temperature=0.7)
)

3. Protein Embeddings with ESM C

Generate high-quality embeddings for downstream tasks like function prediction, classification, or similarity analysis.

When to use:

• Extracting protein representations for machine learning
• Computing sequence similarities
• Feature extraction for protein classification
• Transfer learning for protein-related tasks

Basic usage:

from esm.models.esmc import ESMC
from esm.sdk.api import ESMProtein, LogitsConfig

# Load ESM C model
model = ESMC.from_pretrained("esmc_300m").to("cuda")

# Get embeddings
protein = ESMProtein(sequence="MPRTKEINDAGLIVHSP...")
protein_tensor = model.encode(protein)
logits_output = model.logits(
    protein_tensor,
    LogitsConfig(sequence=True, return_embeddings=True),
)
embeddings = logits_output.embeddings

Batch processing:

# Encode multiple proteins
proteins = [
    ESMProtein(sequence="MPRTKEIND..."),
    ESMProtein(sequence="AGLIVHSPQ..."),
    ESMProtein(sequence="KTEFLNDGR...")
]

embeddings_list = [
    model.logits(
        model.encode(p),
        LogitsConfig(sequence=True, return_embeddings=True),
    ).embeddings
    for p in proteins
]

See references/esm-c-api.md for ESM C model details, efficiency comparisons, and advanced embedding strategies.

4. Function Conditioning and Annotation

Use ESM3's function track to generate proteins with specific functional annotations or predict function from sequence.

Function-conditioned generation:

from esm.sdk.api import ESMProtein, FunctionAnnotation, GenerationConfig

# Create protein with desired function
protein = ESMProtein(
    sequence="_" * 200,  # Generate 200 residue protein
    function_annotations=[
        FunctionAnnotation(label="fluorescent_protein", start=50, end=150)
    ]
)

# Generate sequence with specified function
functional_protein = model.generate(
    protein,
    GenerationConfig(track="sequence", num_steps=200)
)

5. Chain-of-Thought Generation

Iteratively refine protein designs using ESM3's chain-of-thought generation approach.

from esm.sdk.api import GenerationConfig

# Multi-step refinement
protein = ESMProtein(sequence="MPRT" + "_" * 100 + "KEND")

# Step 1: Generate initial structure
config = GenerationConfig(track="structure", num_steps=50)
protein = model.generate(protein, config)

# Step 2: Refine sequence based on structure
config = GenerationConfig(track="sequence", num_steps=50, temperature=0.5)
protein = model.generate(protein, config)

# Step 3: Predict function
config = GenerationConfig(track="function", num_steps=20)
protein = model.generate(protein, config)

6. Batch Processing with Forge API

Process multiple proteins efficiently using Forge's async methods.

import os
import asyncio
import esm
from esm.sdk.api import ESMProtein, GenerationConfig

client = esm.sdk.client("esm3-medium-2024-08", token=os.environ["ESM_API_KEY"])

# Async batch processing
async def batch_generate(proteins_list):
    tasks = [
        client.async_generate(protein, GenerationConfig(track="sequence"))
        for protein in proteins_list
    ]
    return await asyncio.gather(*tasks)

# Execute
proteins = [ESMProtein(sequence=f"MPRT{'_' * 50}KEND") for _ in range(10)]
results = asyncio.run(batch_generate(proteins))

See references/forge-api.md for detailed Forge API documentation, authentication, rate limits, and batch processing patterns.

Model Selection Guide

ESM3 Models (Generative):

• esm3-open (1.4B) - Open weights, local usage after accepting the Hugging Face license
• esm3-medium-2024-08 (7B) - Best balance of quality and speed (Forge only)
• esm3-large-2024-03 (98B) - Highest quality, slower (Forge only)

ESM C Models (Embeddings):

• esmc_300m / esmc-300m-2024-12 (30 layers) - Lightweight, fast inference (open weights, local)
• esmc_600m / esmc-600m-2024-12 (36 layers) - Balanced performance (open weights, local)
• esmc-6b-2024-12 (80 layers) - Maximum quality (Forge API; local 6B weights require Forge or SageMaker)

Local ESMC.from_pretrained() examples use underscore aliases (esmc_300m, esmc_600m). Hosted API clients use dated model IDs such as esmc-600m-2024-12.

Selection criteria:

• Local development/testing: Use esm3-open or esmc_300m
• Production quality: Use esm3-medium-2024-08 via Forge
• Maximum accuracy: Use esm3-large-2024-03 or esmc-6b-2024-12 via Forge
• High throughput: Use Forge or Biohub APIs with explicit async concurrency limits
• Cost optimization: Use smaller models, implement caching strategies

Installation

Install from PyPI (esm on PyPI by EvolutionaryScale). Current PyPI release: 3.2.3 (Oct 14, 2025). Requires Python >=3.12,<3.13.

Basic installation:

uv pip install "esm==3.2.3"

With Flash Attention (recommended for faster inference on NVIDIA GPUs):

uv pip install "esm==3.2.3"
uv pip install flash-attn --no-build-isolation

The Forge client ships with the esm package - no extra install for ESM3 or ESMC Forge inference.

Authentication

Forge API access requires an API key. Never hardcode tokens in scripts or commit them to version control.

1. Check whether ESM_API_KEY is already set in the environment.
2. If not, check a local .env for ESM_API_KEY only (do not load unrelated secrets).
3. If still missing, create a key in the Biohub developer console for Biohub APIs or Forge for legacy Forge-hosted ESM3/ESMC access.

import os

token = os.environ["ESM_API_KEY"]  # raises KeyError if unset

esm.sdk.client() reads ESM_API_KEY automatically when token is omitted. Keep endpoint URLs fixed to trusted hosts such as https://forge.evolutionaryscale.ai or https://biohub.ai; do not take API hosts from untrusted user input.

Biohub platform: EvolutionaryScale and Forge now surface current hosted models through biohub.ai. SDK class names may still reference "Forge". See references/biohub-platform.md for ESMFold2 and Biohub-specific setup.

Common Workflows

For detailed examples and complete workflows, see references/workflows.md which includes:

• Novel GFP design with chain-of-thought
• Protein variant generation and screening
• Structure-based sequence optimization
• Function prediction pipelines
• Embedding-based clustering and analysis

References

This skill includes comprehensive reference documentation:

• references/esm3-api.md - ESM3 model architecture, API reference, generation parameters, and multimodal prompting
• references/esm-c-api.md - ESM C model details, embedding strategies, and performance optimization
• references/forge-api.md - Forge platform documentation, authentication, batch processing, and deployment
• references/biohub-platform.md - Biohub API migration, ESMFold2 structure prediction, and developer-console auth
• references/workflows.md - Complete examples and common workflow patterns

These references contain detailed API specifications, parameter descriptions, and advanced usage patterns. Load them as needed for specific tasks.

Best Practices

For generation tasks:

• Start with smaller models for prototyping (esm3-open)
• Use temperature parameter to control diversity (0.0 = deterministic, 1.0 = diverse)
• Implement iterative refinement with chain-of-thought for complex designs
• Validate generated sequences with structure prediction or wet-lab experiments

For embedding tasks:

• Batch process sequences when possible for efficiency
• Cache embeddings for repeated analyses
• Normalize embeddings when computing similarities
• Use appropriate model size based on downstream task requirements

For production deployment:

• Use Forge API for scalability and latest models
• Implement error handling and retry logic for API calls
• Monitor token usage and implement rate limiting
• Consider AWS SageMaker deployment for dedicated infrastructure

Resources and Documentation

• GitHub Repository: https://github.com/Biohub/esm (current ESMC/ESMFold2/Biohub docs; ESM3 docs remain linked from the repository)
• Forge Platform: https://forge.evolutionaryscale.ai
• Biohub Platform: https://biohub.ai
• Scientific Paper: Hayes et al., Science (2025) - https://www.science.org/doi/10.1126/science.ads0018
• Blog Posts:
• ESM3 Release: https://www.evolutionaryscale.ai/blog/esm3-release
• ESM C Launch: https://www.evolutionaryscale.ai/blog/esm-cambrian
• Community: Slack community at https://join.slack.com/t/esm-community/sharedinvite/zt-2tbndpdmu-92lUCmp8CEOrorioU5qdA
• Model Weights: Hugging Face EvolutionaryScale and Biohub organizations

Composing with the rest of the bundle

• uniprot-rcsb → before: the canonical sequence and any experimental structure. Fold from

sequence only when no structure exists — an experimental structure beats a predicted one.

• protein-binder-design → after: ESM embeddings and likelihoods are inputs to binder design and

filtering, not a substitute for BindCraft or RFdiffusion.

• antibody-engineering → alongside: general protein language models are weak on antibody CDRs,

which are hypervariable by design and poorly represented in the training distribution. Use IMGT numbering and antibody-specific tooling there rather than a generic ESM likelihood.

• immunogenicity → after: a generated or humanised sequence still needs class II epitope scanning.

A low ESM perplexity says nothing about T-cell epitopes.

• boltz / diffdock → after: for a complex, cofolding or docking answers the question ESM does

not — where the ligand goes.

• glycoengineering → after: sequon scanning on any designed sequence destined for expression.
• adaptyv / tamarind → after: designs are hypotheses until expressed and assayed.

Likelihood is not fitness. A high-likelihood sequence is one the model finds typical of its training distribution; that correlates with foldability and not at all with the activity you want.

Responsible Use

ESM is designed for beneficial applications in protein engineering, drug discovery, and scientific research. Follow the Responsible Biodesign Framework (https://responsiblebiodesign.ai/) and Biohub Acceptable Use Policy (https://biohub.org/acceptable-use-policy/) when designing novel proteins. Consider biosafety and ethical implications of protein designs before experimental validation.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: K-Dense-AI
• Source: K-Dense-AI/drug-discovery-agent-skills
• License: MIT
• Homepage: www.k-dense.ai

Install and usage instructions live in the source repository linked above.