boltz
Cofold protein-ligand, protein-protein, and nucleic-acid complexes with Boltz-2, and predict binding affinity with its trained affinity head. Use this skill to build Boltz input YAML, run structure prediction with MSAs, pocket constraints, templates, and modified residues, screen compound libraries by cofolding, and interpret confidence scores (pLDDT, pTM, ipTM, PDE) and affinity output (binder probability and log10 IC50). Also trigger on Boltz, Boltz-1, Boltz-2, cofolding, boltz predict, affinity_pred_value, affinity_probability_binary, ipTM, or open-weights AlphaFold3 alternatives.
- Version
- 1.0
- License
- MIT
- Compatibility
- The bundled scripts need only Python 3.10+ and the standard library. Running a prediction needs boltz 2.2+ (pip install boltz, Python >=3.10 and <3.13) and an NVIDIA GPU; 24 GB is a practical minimum for a typical protein-ligand complex, CPU works but is 50-100x slower. Weights download to ~/.boltz on first run. --use_msa_server sends sequences to the public ColabFold server.
Pinned to revision f67572246d9b, so it is the text this page describes rather than whatever the author pushed since.
Pre-approved tools experimental
Experimental field. Support varies between clients, so this list is what the author declared, not what your client will enforce.
- Read
- Write
- Edit
- Bash
Files
- skills/boltz/SKILL.md
- skills/boltz/references/confidence-and-affinity.md
- skills/boltz/references/running.md
- skills/boltz/references/yaml-schema.md
- skills/boltz/scripts/collect_results.py
- skills/boltz/scripts/make_boltz_yaml.py
- skills/boltz/scripts/screen_library.py
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