Individual skill
esmfold2
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) Predicting complex structures with single-sequence input, (2) Validating designed binders with ESMFold2-Fast, (3) Running ESMFold2 with MSA input, (4) Getting ESMC embeddings or per-residue mutation scores, (5) Choosing kernel backend and sampling-step settings for paper-faithful throughput.
configs/microservice/bff-service/configs/agent-skills/claude-science/esmfold2/SKILL.md
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- Last checked
- Nov 3, 2026
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- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
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# ESMFold2 (Biohub)
All-atom diffusion co-folding from the Biohub ESM release (2026). ESMFold2 =
48 pair layers with MSA support; ESMFold2-Fast = 24 layers, single-sequence
only, ~1.7x faster.
**License:** MIT (code github.com/Biohub/esm + weights HF `biohub/*`).
**Paper:** "Language Modeling Materializes a World Model of Protein Biology" (2026).
## Install
CUDA 12.x GPU (H100/A100-class); Python **3.12 only**. Fresh venv; needs
egress to HF Hub, GitHub, PyPI: