esmfold2

name	esmfold2
description	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.
license	Apache-2.0
origin	openai4s
category	biomodels
requirements	["gpu"]
capabilities	{"network":{"mode":"raw_required","domains":[]}}
metadata	{"display-name":"ESMFold2","third_party":[{"kind":"weights","name":"ESMFold2 / ESMC","provider":"Biohub","license":"MIT","terms_url":"https://github.com/Biohub/esm/blob/main/LICENSE.md"}]}

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 NVIDIA NIM env (remote-compute-nvidia skill) is the canonical, version-pinned recipe.

Gotchas:

• Default kernel backend is None (reference PyTorch, ~12x slower than paper). Call model.set_kernel_backend('fused') after from_pretrained(). See section below.
• Match torch CUDA build to your driver; the pin <2.8 targets 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.

Usage — local model

from esm.models.esmfold2 import (
    ESMFold2InputBuilder, StructurePredictionInput,
    ProteinInput, DNAInput, RNAInput, LigandInput, Modification,
)
 transformers.models.esmfold2.modeling_esmfold2  ESMFold2Model

model = ESMFold2Model.from_pretrained().cuda().()



spi = StructurePredictionInput(sequences=[
    ProteinInput(=, sequence=target_seq),
    ProteinInput(=, sequence=binder_seq),
    
    
    
])


results = ESMFold2InputBuilder().fold(
    model, spi,
    num_loops=,             
    num_sampling_steps=,    
    num_diffusion_samples=,  
    seed=,
)



best = (results, key= r: (r.iptm  r.iptm   
                                         r.plddt.mean()))
(, ).write(best..to_mmcif())