---
name: bindcraft
description: >
  End-to-end binder design using BindCraft hallucination. Use this skill when:
  (1) Designing protein binders with built-in AF2 validation,
  (2) Running production-quality binder campaigns,
  (3) Using different design protocols (fast, default, slow),
  (4) Need joint backbone and sequence optimization,
  (5) Want high experimental success rate.

  For backbone-only generation, use rfdiffusion.
  For QC thresholds, use protein-qc.
  For tool selection guidance, use binder-design.
license: MIT
category: design-tools
tags: [structure-design, sequence-design, binder, pipeline]
proteinbase_slug: bindcraft
proteinbase_url: https://proteinbase.com/design-methods/bindcraft
biomodals_script: modal_bindcraft.py
---

# BindCraft Binder Design

## Prerequisites

| Requirement | Minimum | Recommended |
|-------------|---------|-------------|
| Python | 3.9+ | 3.10 |
| CUDA | 11.7+ | 12.0+ |
| GPU VRAM | 32GB | 48GB (L40S) |
| RAM | 32GB | 64GB |

## How to run

> **First time?** See [Getting started](../../docs/getting-started.md) to set up Modal and biomodals.

### Option 1: Modal (recommended)
```bash
cd biomodals
modal run modal_bindcraft.py \
  --input-pdb target.pdb \
  --target-chains A \
  --target-hotspot-residues "45,67,89" \
  --lengths "70,100" \
  --number-of-final-designs 50
```

**GPU**: L40S (48GB) | **Timeout**: 300 min default

### Option 2: Local installation
```bash
git clone https://github.com/martinpacesa/BindCraft.git
cd BindCraft

# BindCraft is configured with JSON files, not flags
python -u ./bindcraft.py \
  --settings ./settings_target/mytarget.json \
  --filters ./settings_filters/default_filters.json \
  --advanced ./settings_advanced/default_4stage_multimer.json
```

The target PDB, chains, hotspots, and binder length range are set inside the
`--settings` JSON. See the BindCraft repo for the settings schema.

## Key parameters (Modal wrapper)

| Parameter | Default | Description |
|-----------|---------|-------------|
| `--input-pdb` | required | Target structure |
| `--target-chains` | `A` | Target chain(s) |
| `--target-hotspot-residues` | "" | Target hotspots (e.g. "45,67,89") |
| `--lengths` | `50,130` | Binder length range |
| `--number-of-final-designs` | 1 | Passing designs to return |
| `--max-trajectories` | none | Cap on trajectories |

## Output format

```
output/
├── design_0/
│   ├── binder.pdb         # Final design
│   ├── complex.pdb        # Binder + target
│   ├── metrics.json       # QC scores
│   └── trajectory/        # Optimization trajectory
├── design_1/
│   └── ...
└── summary.csv            # All metrics
```

### Metrics Output
```json
{
  "plddt": 0.89,
  "ptm": 0.78,
  "iptm": 0.62,
  "pae": 8.5,
  "rmsd": 1.2,
  "sequence": "MKTAYIAK..."
}
```

## Sample output

### Successful run
```
$ modal run modal_bindcraft.py --input-pdb target.pdb --target-chains A --target-hotspot-residues "45,67,89" --number-of-final-designs 50
[INFO] Loading BindCraft model...
[INFO] Target: target.pdb (chain A)
[INFO] Hotspots: 45, 67, 89
[INFO] Generating designs...

Design 1/50:
  Length: 78 AA
  pLDDT: 0.89, ipTM: 0.62
  Saved: output/design_0/

Design 50/50:
  Length: 85 AA
  pLDDT: 0.86, ipTM: 0.58
  Saved: output/design_49/

[INFO] Campaign complete. Summary: output/summary.csv
Pass rate: 32/50 (64%) with ipTM > 0.5
```

**What good output looks like:**
- pLDDT: > 0.85 for most designs
- ipTM: > 0.5 for passing designs
- Pass rate: 30-70% depending on target
- Diverse sequences across designs

## Decision tree

```
Should I use BindCraft?
│
├─ What type of design?
│  ├─ Production-quality binders → BindCraft ✓
│  ├─ High diversity exploration → RFdiffusion
│  └─ All-atom precision → BoltzGen
│
├─ What matters most?
│  ├─ Experimental success rate → BindCraft ✓
│  ├─ Speed / diversity → RFdiffusion + ProteinMPNN
│  ├─ AF2 gradient optimization → ColabDesign
│  └─ All-atom control → BoltzGen
│
└─ Compute resources?
   ├─ Have L40S/A100 → BindCraft ✓
   └─ Only A10G → RFdiffusion + ProteinMPNN
```

## Typical performance

| Campaign Size | Time (L40S) | Cost (Modal) | Notes |
|---------------|-------------|--------------|-------|
| 50 designs | 2-4h | ~$15 | Quick campaign |
| 100 designs | 4-8h | ~$30 | Standard |
| 200 designs | 8-16h | ~$60 | Large campaign |

Adaptyv's own tests of these models showed BindCraft costing about $2.90 per accepted
design, averaged across 7 targets.

**Experimental success rate** (BindCraft paper): 10 to 100%, averaging 46.3% across 12
targets; strongly target-dependent.

---

## Verify

```bash
find output -name "binder.pdb" | wc -l  # Should match num_designs
```

---

## Troubleshooting

**Low ipTM scores**: Check hotspot selection, increase designs
**Slow convergence**: Use fast protocol for screening
**OOM errors**: Reduce num_models, use L40S GPU
**Poor diversity**: Lower sampling_temp, run multiple seeds

### Error interpretation

| Error | Cause | Fix |
|-------|-------|-----|
| `RuntimeError: CUDA out of memory` | Large target or long binder | Use L40S/A100, reduce binder length |
| `ValueError: no hotspots` | Hotspots not found | Check residue numbering |
| `TimeoutError` | Design taking too long | Use fast protocol |

---

**Next**: Rank by `ipsae` → experimental validation.