rfdiffusion
Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill rfdiffusion --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.
# RFdiffusion Backbone Generation ## Prerequisites | Requirement | Minimum | Recommended | |-------------|---------|-------------| | Python | 3.9+ | 3.10 | | CUDA | 11.7+ | 12.0+ | | GPU VRAM | 16GB | 24GB (A10G) | | RAM | 16GB | 32GB | ## How to run > **First time?** See [Installation Guide](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: Modal (recommended) ```bash # Clone biomodals git clone https://github.com/hgbrian/biomodals && cd biomodals # Basic binder design modal run modal_rfdiffusion.py \ --pdb target.pdb \ --contigs "A1-150/0 70-100" \ --hotspot "A45,A67,A89" \ --num-designs 100 # With custom GPU/timeout GPU=A100 TIMEOUT=60 modal run modal_rfdiffusion.py \ --pdb target.pdb \ --contigs "A1-150/0 70-100" \ --num-designs 100 ``` **GPU**: A10G (24GB) | **Timeout**: 30min default ### Option 2: Local installation ```bash # Clone and install git clone https://github.com/RosettaCommons/RFdiffusion.git cd RFdiffusion && pip install -e . # Download weights wget http://files.ipd.uw.edu/pub/RFdiffusion/models/Complex_base_ckpt.pt # Run inference python run_inference.py \ inference.input_pdb=target.pdb \ contigmap.contigs=[A1-150/0 70-100] \ ppi.hotspot_res
- Prerequisites
- How to run
- Option 1: Modal (recommended)
- Option 2: Local installation
- Config Schema (Hydra)
- Contigmap Syntax
- Hotspot Specification
- Common mistakes
- Contig Syntax
- Hotspot Residues
- Complete Parameter Reference
- Model Checkpoints
- Common workflows
- Binder Design
Clone biomodals git clone https://github.com/hgbrian/biomodals && cd biomodals Basic binder design modal run modal_rfdiffusion.py \ With custom GPU/timeout Clone and install git clone https://github.com/RosettaCommons/RFdiffusion.git cd RFdiffusion && pip install -e . Download weights wget http://files.ipd.uw.edu/pub/RFdiffusion/models/Complex_base_ckpt.pt
What does the rfdiffusion skill do?
Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill rfdiffusion --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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.