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 ESMFol
Profile →npx skills add xuzhougeng/wisp-science --skill esmfold2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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: ```bash pip install --no-cache-dir uv uv venv --python 3.12 /work/venv && source /work/venv/bin/activate uv pip install \ "torch>=2.5,<2.8" einops "biotite>=1.0" rdkit msgpack-numpy biopython \ scikit-learn brotli attrs pandas cloudpathlib httpx tenacity zstd pydssp \ pygtrie accelerate huggingface_hub safetensors "numpy<3" networkx \ sentencepiece tokenizers regex packaging filelock pyyaml typing_extensions \ "transformers @ git+https://github.com/Biohub/transformers.git@3a8956fb4d4ea16b0ec8e71deef2c2909b6a5cbf" uv pip install --no-deps "esm @ git+https://github.com/Biohub/esm.git@f652b471" # OPTIONAL — only affects ESMC attention; trunk speedup comes from set_kernel_backend("fused") uv pip install ninja packaging wheel setu
- Install
- Wisp execution
- Usage — local model
- Model variants on HF biohub/
- Throughput: setkernelbackend("fused") is REQUIRED
- ESMFold2-Experimental — design hook
- Gotcha: cusolver SVD poison + structseq constructor
- With-MSA mode
- Paper-matched inference configuration
- Training data cutoff
- ESMC language model
pip install --no-cache-dir uv
uv venv --python 3.12 /work/venv && source /work/venv/bin/activate
uv pip install \
scikit-learn brotli attrs pandas cloudpathlib httpx tenacity zstd pydssp \
pygtrie accelerate huggingface_hub safetensors "numpy<3" networkx \
sentencepiece tokenizers regex packaging filelock pyyaml typing_extensions \
uv pip install --no-deps "esm @ git+https://github.com/Biohub/esm.git@f652b471"
OPTIONAL — only affects ESMC attention; trunk speedup comes from set_kernel_backend("fused")
uv pip install ninja packaging wheel setuptools
Do NOT install transformer-engine — RuntimeError (not ImportError) on importWhat does the esmfold2 skill do?
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 ESMFol
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill esmfold2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From xuzhougeng/wisp-science, a repository with 895 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.