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esm

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Toolkit for protein language models (ESM3 for multimodal generative protein design; ESM C for efficient embeddings). Use when you need sequence/structure/function generation or prediction, inverse folding, protein embeddings, or scalable inference via local weights or the Forge API.

scientific-skills/Protocol Design/esm/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

## When to Use

- **Designing novel proteins** with desired properties by generating sequences (optionally conditioned on structure/function) using **ESM3**.
- **Completing or editing sequences** (e.g., filling masked residues, generating variants) for protein engineering workflows.
- **Predicting 3D structure from sequence** or performing **inverse folding** (designing sequences for a target structure) with ESM3’s structure/sequence tracks.
- **Generating protein embeddings** for downstream ML tasks (classification, clustering, similarity search, function prediction) using **ESM C**.
- **Scaling inference** to many sequences using the **Forge API** (async/batch execution, hosted large models).

## Key Features

- **ESM3 multimodal generation** across *sequence*, *structure*, and *function* tracks.
- **Local inference** (e.g., `esm3-sm-open-v1`) and **cloud inference** via **Forge** (e.g., `esm3-medium-2024-08`, `esm3-large-2024-03`).
- **Structure prediction** (sequence → coordinates/PDB) and **inverse folding** (structure → designed sequence).
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