Option 1: Modal

cd biomodals
modal run modal_boltz.py \
  --input-faa complex.fasta \
  --out-dir predictions/

GPU: L40S (48GB) | Timeout: 1800s default

Option 2: Local installation

pip install boltz

boltz 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 format

predictions/
├── model_0.cif       # Best model (CIF format)
├── confidence.json   # pLDDT, pTM, ipTM
└── pae.npy          # PAE matrix

Note: Boltz outputs CIF format. Convert to PDB if needed:

from Bio.PDB import MMCIFParser, PDBIO
parser = MMCIFParser()
structure = parser.get_structure("model", "model_0.cif")
io = PDBIO()
io.set_structure(structure)
io.save("model_0.pdb")

Comparison

Feature	Boltz-1	Boltz-2	AF2-Multimer
MSA-free mode	Yes	Yes	No
Diffusion	Yes	Yes	No
Speed	Fast	Faster	Slower
Open source	Yes	Yes	Yes

Sample output

Successful run

$ boltz predict --fasta complex.fasta --output predictions/
[INFO] Loading Boltz-1 weights...
[INFO] Predicting structure...
[INFO] Saved model to predictions/model_0.cif

predictions/confidence.json:
{
  "ptm": 0.78,
  "iptm": 0.65,
  "plddt": 0.81
}

What good output looks like:

pTM: > 0.7 (confident global structure)
ipTM: > 0.5 (confident interface)
pLDDT: > 0.7 (confident per-residue)
CIF file: ~100-500 KB for typical complex

Decision tree

Should I use Boltz?
│
├─ What are you predicting?
│  ├─ Protein-protein complex → Boltz ✓ or Chai or ColabFold
│  ├─ Protein + ligand → Boltz ✓ or Chai
│  └─ Single protein → Use ESMFold (faster)
│
├─ Need MSA?
│  ├─ No / want speed → Boltz ✓
│  └─ Yes / maximum accuracy → ColabFold
│
└─ Why Boltz over Chai?
   ├─ Open weights preference → Boltz ✓
   ├─ Boltz-2 speed → Boltz ✓
   └─ DNA/RNA support → Consider Chai

Typical performance

Campaign Size	Time (L40S)	Cost (Modal)	Notes
100 complexes	30-45 min	~$8	Standard validation
500 complexes	2-3h	~$35	Large campaign
1000 complexes	4-6h	~$70	Comprehensive

Per-complex: ~15-30s for typical binder-target complex.

Verify

find predictions -name "*.cif" | wc -l  # Should match input count

Troubleshooting

Low confidence: Increase recycling_steps OOM errors: Use MSA-free mode or A100-80GB Slow prediction: Reduce sampling_steps

Error interpretation

Error	Cause	Fix
`RuntimeError: CUDA out of memory`	Complex too large	Use `--use_msa_server false` or larger GPU
`KeyError: 'iptm'`	Single chain only	Ensure FASTA has 2+ chains
`FileNotFoundError: weights`	Missing model	Run `boltz download` first
`ValueError: invalid residue`	Non-standard AA	Check for modified residues in sequence

Boltz-1 vs Boltz-2

Aspect	Boltz-1	Boltz-2
Speed	Fast	Faster
Accuracy	Good	Improved, notably antibody-antigen
Ligands	Basic	Better support
Affinity prediction	No	Yes (small-molecule binding)
Release	2024	2025

Boltz-2 is the current default. Boltz-1 is still used where a design pipeline inverts the v1 model.

Affinity prediction (Boltz-2)

Boltz-2 adds an affinity-prediction module that approaches free-energy-perturbation accuracy at a fraction of the cost. It is trained on small-molecule binding data, so use it for protein-ligand and small-molecule work. It does not predict protein-protein binding affinity; for protein binders, rely on interface confidence (ipTM, ipSAE) instead.

Next: protein-qc for filtering and ranking.

Requirement	Minimum	Recommended
Python	3.10+	3.11
CUDA	12.0+	12.1+
GPU VRAM	24GB	48GB (L40S)
RAM	32GB	64GB

boltz

Boltz Structure Prediction

Prerequisites

How to run