# Bindcraft

End-to-end binder design using BindCraft hallucination.

- Repository: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills (3090 stars)
- Category: Design
- License: MIT
- Last updated: 2026-07-21
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bindcraft -g -a claude-code -y`
Codex: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bindcraft -g -a codex -y`
Cursor: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bindcraft -g -a cursor -y`
Gemini CLI: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bindcraft -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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 [Installation Guide](../../docs/installation.md) to set up Modal and biomodals.

### Option 1: Modal (recommended)
```bash
cd biomodals
modal run modal_bindcraft.py \
  --target-pdb target.pdb \
  --target-chain A \
  --binder-lengths 70-100 \
  --hotspots "A45,A67,A89" \
  --num-designs 50
```

**GPU**: L40S (48GB) | **Timeout**: 3600s default

### Option 2: Local installation
```bash
git clone https://github.com/martinpacesa/BindCraft.git
cd BindCraft
pip install -r requirements.txt

python bindcraft.py \
  --target target.pdb \
  --target_chains A \
  --binder_lengths 70-100 \
  --hotspots A45,A67,A89 \
  --num_designs 50
```

## Key parameters

| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| `--target-pdb` | required | path | Target structure |
| `--target-chain` | required | A-Z | Target chain(s) |
| `--binder-lengths` | 70-100 | 40-150 | Length range |
| `--hotspots` | None | residues | Target hotspots |
| `--num-designs` | 50 | 1-500 | Number of designs |
| `--protocol` | default | fast/default/slow | Quality vs speed |

## Protocols

| Protocol | Speed | Quality | Use Case |
|----------|-------|---------|----------|
| fast | Fast | Lower | Initial screening |
| default | Medium | Good | Standard campaigns |
| slow | Slow | High | Final production |

## 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
```

The rest: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/blob/HEAD/skills/bindcraft/SKILL.md