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:

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:

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:

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

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:

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:

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 executor.

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:

uv pip install esm

With Flash Attention (recommended for faster inference):

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

For Forge API access:

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.