ESM: Evolutionary Scale Modeling

Overview

ESM provides state-of-the-art protein language models for understanding, generating, and designing proteins. This skill enables working with two model families: ESM3 for generative protein design across sequence, structure, and function, and ESM C for efficient protein representation learning and embeddings.

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:

python

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

# Load model locally
model: ESM3InferenceClient = ESM3.from_pretrained("esm3-sm-open-v1").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:

python

from esm.sdk.forge import ESM3ForgeInferenceClient
from esm.sdk.api import ESMProtein, GenerationConfig

# Connect to Forge
model = ESM3ForgeInferenceClient(model="esm3-medium-2024-08", url="https://forge.evolutionaryscale.ai", token="<token>")

# 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:

python

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):

python

# 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:

python

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

# Load ESM C model
model = ESMC.from_pretrained("esmc-300m").to("cuda")

# Get embeddings
protein = ESMProtein(sequence="MPRTKEINDAGLIVHSP...")
protein_tensor = model.encode(protein)

# Generate embeddings
embeddings = model.forward(protein_tensor)

Batch processing:

python

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

embeddings_list = [model.logits(model.forward(model.encode(p))) 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:

python

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.

python

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 executor.

python

from esm.sdk.forge import ESM3ForgeInferenceClient
import asyncio

client = ESM3ForgeInferenceClient(model="esm3-medium-2024-08", token="<token>")

# 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-sm-open-v1
```
(1.4B) - Open weights, local usage, good for experimentation
```
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
```
(30 layers) - Lightweight, fast inference
```
esmc-600m
```
(36 layers) - Balanced performance
```
esmc-6b
```
(80 layers) - Maximum representation quality

Selection criteria:

Local development/testing: Use
```
esm3-sm-open-v1
```
or
```
esmc-300m
```
Production quality: Use
```
esm3-medium-2024-08
```
via Forge
Maximum accuracy: Use
```
esm3-large-2024-03
```
or
```
esmc-6b
```
High throughput: Use Forge API with batch executor
Cost optimization: Use smaller models, implement caching strategies

Installation

Basic installation:

bash

uv pip install esm

With Flash Attention (recommended for faster inference):

bash

uv pip install esm
uv pip install flash-attn --no-build-isolation

For Forge API access:

bash

uv pip install esm  # SDK includes Forge client

No additional dependencies needed. Obtain Forge API token at https://forge.evolutionaryscale.ai

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/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-sm-open-v1
```
)
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/evolutionaryscale/esm
Forge Platform: https://forge.evolutionaryscale.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://bit.ly/3FKwcWd
Model Weights: HuggingFace EvolutionaryScale organization

Responsible Use

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

esm

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SKILL.md Content

ESM: Evolutionary Scale Modeling

Overview

Core Capabilities

1. Protein Sequence Generation with ESM3

2. Structure Prediction and Inverse Folding

3. Protein Embeddings with ESM C

4. Function Conditioning and Annotation

5. Chain-of-Thought Generation

6. Batch Processing with Forge API

Model Selection Guide

Installation

Common Workflows

References

Best Practices

Resources and Documentation

Responsible Use