Esmfold2
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al.
Install
npx skills add aipoch/open-science --skill esmfold2 -g -a claude-code -y- Repository
- aipoch/open-science5.4k
- Category
- Design
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 2,394 tokens
- Runs scripts
- No, instructions only
What it does
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.
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:
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 setuptools
MAX_JOBS=8 uv pip install --no-deps --no-build-isolation "flash-attn<3"
# Do NOT install transformer-engine, RuntimeError (not ImportError) on import
# slips ESMC's guard and kills ESMFold2Model import.
The bundled esmfold2_gpu Modal env (remote-compute-modal skill) is the
canonical, version-pinned recipe.
Gotchas:
- Default kernel backend is
None(reference PyTorch, ~12x slower than paper). Callmodel.set_kernel_backend('fused')afterfrom_pretrained(). See section below. - Match torch CUDA build to your driver; the pin
<2.8targets CUDA 12.2. - Weights via Xet bridge ~300 MB/s: ESMFold2 1.36 GB, ESMFold2-Fast 0.76 GB. Set
HF_HOME=/work/hf_cache.
