Skills

Bindcraft

End-to-end binder design using BindCraft hallucination.

Install

npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bindcraft -g -a claude-code -y
Category
Design
Updated
4mo ago
License
MIT
Context cost
Lightabout 1,080 tokens
Runs scripts
No, instructions only

What it does

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.

BindCraft Binder Design

Prerequisites

RequirementMinimumRecommended
Python3.9+3.10
CUDA11.7+12.0+
GPU VRAM32GB48GB (L40S)
RAM32GB64GB

How to run

First time? See Installation Guide to set up Modal and biomodals.

Option 1: Modal (recommended)

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

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

ParameterDefaultRangeDescription
--target-pdbrequiredpathTarget structure
--target-chainrequiredA-ZTarget chain(s)
--binder-lengths70-10040-150Length range
--hotspotsNoneresiduesTarget hotspots
--num-designs501-500Number of designs
--protocoldefaultfast/default/slowQuality vs speed

Protocols

ProtocolSpeedQualityUse Case
fastFastLowerInitial screening
defaultMediumGoodStandard campaigns
slowSlowHighFinal 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
Read the whole SKILL.md on GitHub

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