Is this skill suitable for biological research?

Yes, it is designed for beneficial applications in protein engineering, drug discovery, and scientific research, following the Responsible Biodesign Framework.

Can I perform inverse folding with this Claude skill?

Absolutely. You can use the ESM3 structure track to provide a PDB structure and generate sequences that are predicted to fold into that specific target configuration.

What are the requirements for using the Forge API?

To use Forge, you need to obtain an API token from evolutionaryscale.ai and use the ESM3ForgeInferenceClient included in the skill's SDK.

What is the difference between ESM3 and ESM C in this skill?

ESM3 is a multimodal generative model designed for protein creation across sequence, structure, and function, while ESM C is optimized for generating efficient protein embeddings and representations for analysis.

Does this skill support local inference for protein models?

Yes, the skill supports local model usage for open-weight versions like esm3-sm-open-v1, as well as remote inference via the EvolutionaryScale Forge API.

ESM Protein Design & Modeling

Name: ESM Protein Design & Modeling
Author: Microck

byMicrock

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Ciencia de Datos y ML

Enables advanced protein engineering through generative design, structure prediction, and high-performance embeddings using ESM3 and ESM C models.

Acerca de

This skill integrates Evolutionary Scale Modeling (ESM) capabilities into Claude, providing a comprehensive toolkit for state-of-the-art protein language modeling. It supports ESM3 for multimodal generative design—allowing users to manipulate sequence, structure, and function tracks simultaneously—and ESM C for efficient protein representation learning. Whether performing inverse folding, generating novel protein sequences with specific functional annotations, or extracting high-dimensional embeddings for downstream machine learning tasks, this skill offers a streamlined interface for both local model deployment and scalable cloud-based inference via the Forge API.

Características Principales

High-quality protein embeddings and representations via ESM C
81 GitHub stars
Function-conditioned protein generation and annotation
Generative protein design across sequence, structure, and function tracks
Inverse folding and structure-based sequence optimization
Support for local inference and scalable cloud-based Forge API integration

Casos de Uso

Designing novel proteins with specific structural or functional properties
Predicting protein structures and generating embeddings for downstream ML tasks
Automating protein engineering workflows and variant generation

Acerca de

Características Principales

High-quality protein embeddings and representations via ESM C
81 GitHub stars
Function-conditioned protein generation and annotation
Generative protein design across sequence, structure, and function tracks
Inverse folding and structure-based sequence optimization
Support for local inference and scalable cloud-based Forge API integration

Casos de Uso

Designing novel proteins with specific structural or functional properties
Predicting protein structures and generating embeddings for downstream ML tasks
Automating protein engineering workflows and variant generation