boltz-structure-prediction
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-design-qc. For AlphaFold2 prediction, use alphafold2-multimer. For Chai prediction, use chai1-structure-prediction.
npx skills add BioTender-max/awesome-bio-agent-skills --skill boltz-structure-prediction --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Boltz-1 / Boltz-2 Structure Prediction **Plain-language role**: Use Boltz when you want an open-source structure predictor for protein or protein-ligand validation. ## Prerequisites | Requirement | Minimum | Recommended | |-------------|---------|-------------| | Python | 3.10+ | 3.11 | | CUDA | 12.0+ | 12.1+ | | GPU VRAM | 24GB | 48GB (L40S) | | RAM | 32GB | 64GB | ## How to run > **First time?** See [Installation Guide](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: Modal ```bash cd biomodals modal run modal_boltz.py \ --input-faa complex.fasta \ --out-dir predictions/ ``` **GPU**: L40S (48GB) | **Timeout**: 1800s default ### Option 2: Local installation ```bash pip install boltz-structure-prediction boltz-structure-prediction predict \ --fasta complex.fasta \ --output predictions/ ``` ## Key parameters | Parameter | Default | Range | Description | |-----------|---------|-------|-------------| | `--recycling_steps` | 3 | 1-10 | Recycling iterations | | `--sampling_steps` | 200 | 50-500 | Diffusion steps | | `--use_msa_server` | true | bool | Use MSA server | ## FASTA Format ``` >protein_A MKTAYIAKQRQISFVK... >protein_B MVLSPADKTNVKAAWG... ``` ## Output
- Prerequisites
- How to run
- Option 1: Modal
- Option 2: Local installation
- Key parameters
- FASTA Format
- Output format
- Comparison
- Sample output
- Successful run
- Decision tree
- Typical performance
- Verify
- Troubleshooting
cd biomodals modal run modal_boltz.py \ pip install boltz-structure-prediction boltz-structure-prediction predict \ find predictions -name "*.cif" | wc -l # Should match input count
What does the boltz-structure-prediction skill do?
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-design-qc. For AlphaFold2 prediction, use alphafold2-multimer. For Chai prediction, use chai1-structure-prediction.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill boltz-structure-prediction --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.