domains / families / science / esm

esm

combined

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference. Uses the Biohub esm Python SDK for ESM3 protein generation, ESMC embeddings, and ESMFold2 all-atom folding. Applies to local model inference and Biohub hosted clients, including former Forge workflows; distinguishes the separate legacy fair-esm distribution.

When to use

• Use for Biohub's `esm` SDK: ESM3 masked multimodal generation, ESMC sequence representations, and ESMFold2 all-atom prediction. The former Forge platform migrated to `https://biohub.ai`, including ESM3; SDK class names still contain `Forge…
• **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).

Skill page

Conflict

The three skills cover the same domain (ESM protein models) but diverge significantly on the current state of the SDK and API. The K-Dense-AI skill is the most up-to-date, explicitly targeting `esm 3.4.1.post1` and the migration from 'Forge' to 'Biohub', while the other two rely on the older 'Forge' API and deprecated class names (e.g., `ESMC` vs `EsmcForMaskedLM`). The K-Dense skill also provides critical safety constraints (isolated environments, error handling for `ESMProteinError`) that the others lack. Conversely, the other skills provide specific implementation patterns for 'Chain-of-Thought' generation and inverse folding that are not detailed in the K-Dense skill. Because the K-Dense skill is the 'authentic' current procedure but the others contain unique workflow steps (CoT, specific inverse folding examples) that are not contradictory but additive, and because the K-Dense skill's strict version pinning conflicts with the others' generic installation advice, a combined instruction is necessary to provide a complete, safe, and up-to-date guide.