boltz

name	boltz
description	Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or CCD ligand, or to get an open-source AlphaFold3 alternative with optional binding-affinity prediction.
license	Apache-2.0
origin	openai4s
category	biomodels
requirements	["gpu"]
capabilities	{"network":{"mode":"raw_required","domains":[]}}
metadata	{"third_party":[{"kind":"weights","name":"Boltz-2","license":"MIT","terms_url":"https://github.com/jwohlwend/boltz/blob/main/LICENSE"},{"kind":"service","name":"ColabFold MSA server (api.colabfold.com)","provider":"Steinegger Lab","info_url":"https://github.com/sokrypton/ColabFold/wiki"}]}

Boltz-2

Boltz-2 is the open-weights diffusion co-folder closest in surface to AlphaFold3: a YAML describing protein, DNA, RNA, and ligand chains in, mmCIF plus pTM/ipTM/pLDDT confidences out, with an optional small-molecule affinity head. Among our four co-fold skills it is the default for binder-validation campaigns — fully open MIT weights and the fastest sampler; pick chai1 when you want a second independent model for consensus, openfold3 when AF3-faithful settings matter, and esmfold2 when you can live without an MSA. Code and weights are MIT (PyPI boltz, github.com/jwohlwend/boltz).

Running it

# complex.yaml
version: 1
sequences:
  - protein:
      id: A
      sequence: MVTPEGNVSLVDESLLVGVTDEDRAVRS...   # target
  - protein:
      id: B
      sequence: AIQRTPKIQVYSRHPAENG...            # binder
  - ligand:
      id: L
      smiles: 'N[C@@H](Cc1ccc(O)cc1)C(=O)O'      # or  ccd: SAH

boltz predict complex.yaml \
    --use_msa_server --out_dir out/ --recycling_steps 3 --diffusion_samples 5

Each protein chain needs an MSA; without one the run exits before the model loads. --use_msa_server queries api.colabfold.com (expect a 30–90 s pause per chain) and is the right default unless you already have an .a3m to name under msa: in the YAML. Setting msa: empty forces single-sequence mode — that is an accuracy sacrifice, not a speed or memory optimization, because the MSA search runs on CPU before the GPU stage starts.

Per input the output lands at out/boltz_results_complex/predictions/complex/. Read first: > 0.5 is the community pass line for an interface, > 0.7 for the fold itself, and is the weighted aggregate the structures are ranked by. Structures themselves are (or with ).