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 category: biomodels requirements: [gpu] metadata:
github.com/jwohlwend/boltz/blob/main/LICENSE: MIT (© 2024 Wohlwend, Corso,
Passaro). verified 2026-06-30
third_party:
- kind: weights
name: Boltz-2
license: MIT
terms_url: https://github.com/jwohlwend/boltz/blob/main/LICENSE
# SKILL.md — --use_msa_server queries api.colabfold.com; the doc says to
# add it when a chain has no MSA. User's sequence is POSTed there. No
# published ToS — wiki is the closest data-use reference.
- 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 , github.com/jwohlwend/boltz).