freedomintelligence/openclaw-medical-skills/rfdiffusion

Name: rfdiffusion
Author: freedomintelligence

Claude Code and agent skill by freedomintelligence/openclaw-medical-skills

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By freedomintelligence
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freedomintelligence/openclaw-medical-skills
skills/rfdiffusion/SKILL.md
Updated 23 Sep 2026
Version 1.0.01 version recorded

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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.

Install

npx vskill@latest install freedomintelligence/openclaw-medical-skills/rfdiffusion

SKILL.md

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 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=[A45,A67,A89] \
  inference.num_designs=100

Config Schema (Hydra)

Contigmap Syntax

bash

# De novo single chain (50-100 residues)
contigmap.contigs=[50-100]
 
# Binder + target (A = target chain, fixed with /0)
contigmap.contigs=[A1-150/0 70-100]
 
# Motif scaffolding (preserve residues, /0 = fixed)
contigmap.contigs=[20-40/0 A10-30/0 20-40]
 
# Multi-chain binder
contigmap.contigs=[A1-100/0 B1-100/0 60-80]
 
# Variable length ranges
contigmap.contigs=[A1-150/0 50-100]  # Binder 50-100 AA

Hotspot Specification

bash

# Residues for interface (chain + resnum, no spaces)
ppi.hotspot_res=[A45,A67,A89]

Common mistakes

Contig Syntax

✅ Correct:

bash

contigmap.contigs=[A1-150/0 70-100]  # Target fixed (/0), binder variable

❌ Wrong:

bash

contigmap.contigs=[A1-150 70-100]    # Missing /0 - target will move!
contigmap.contigs="A1-150/0 70-100"  # Quotes break parsing
contigmap.contigs=[A1-150/0, 70-100] # Comma breaks parsing

Hotspot Residues

✅ Correct:

bash

ppi.hotspot_res=[A45,A67,A89]        # Chain letter + residue number

❌ Wrong:

bash

ppi.hotspot_res=[45,67,89]           # Missing chain letter
ppi.hotspot_res=[A45, A67, A89]      # Spaces break parsing
ppi.hotspot_res="A45,A67,A89"        # Quotes break parsing

Complete Parameter Reference

Core Parameters

Parameter	Default	Range	Description
`inference.num_designs`	10	1-10000	Number of designs to generate
`inference.input_pdb`	-	path	Target structure file
`inference.output_prefix`	output	string	Output filename prefix
`diffuser.T`	50	20-200	Diffusion timesteps
`denoiser.noise_scale_ca`	1.0	0.0-2.0	CA atom noise (0.5-0.8 = conservative)
`denoiser.noise_scale_frame`	1.0	0.0-2.0	Frame noise
`inference.ckpt_override_path`	-	path	Model checkpoint
`potentials.guide_scale`	1.0	0.1-10	Guidance strength
`potentials.guide_decay`	constant	string	Decay type

Advanced Parameters

Parameter	Default	Description
`diffuser.partial_T`	None	Start diffusion from timestep T (partial diffusion)
`contigmap.inpaint_str`	None	Sequence positions to inpaint
`scaffoldguided.scaffoldguided`	false	Enable scaffold-guided generation
`scaffoldguided.target_pdb`	None	Scaffold template PDB
`ppi.binderlen`	None	Specify exact binder length

Symmetry Parameters

Parameter	Default	Description
`symmetry.symmetry`	None	Symmetry type (C2, C3, C4, D2, etc.)
`symmetry.recenter`	true	Recenter symmetric assembly
`symmetry.radius`	None	Radius constraint for symmetric assembly

Fold Conditioning

Parameter	Default	Description
`contigmap.provide_seq`	None	Provide sequence for fold conditioning
`contigmap.inpaint_seq`	None	Positions for sequence inpainting

Model Checkpoints

Checkpoint	Use Case
`Complex_base_ckpt.pt`	Binder design (default)
`Base_ckpt.pt`	De novo monomers
`ActiveSite_ckpt.pt`	Active site scaffolding
`InpaintSeq_ckpt.pt`	Sequence inpainting

Common workflows

Binder Design

Prepare target PDB (trim to binding region + 10A buffer)
Identify 3-6 hotspot residues (exposed, conserved)
Generate 100-500 backbones
Pass to proteinmpnn for sequence design

Motif Scaffolding

Extract motif coordinates
Use /0 to fix motif in contigmap
Generate surrounding scaffold
Validate motif preservation (RMSD < 1.5A)

Symmetric Oligomers

bash

# C3 symmetric trimer
python run_inference.py \
  symmetry.symmetry=C3 \
  contigmap.contigs=[100-150] \
  inference.num_designs=50
 
# D2 symmetric tetramer
python run_inference.py \
  symmetry.symmetry=D2 \
  contigmap.contigs=[80-120] \
  symmetry.radius=25
 
# Supported symmetries: C2, C3, C4, C5, C6, D2, D3, D4, tetrahedral, octahedral

Partial Diffusion (Refinement)

bash

# Start from existing structure, diffuse from timestep 10
python run_inference.py \
  inference.input_pdb=initial.pdb \
  diffuser.partial_T=10 \
  contigmap.contigs=[A1-100]

Output format

output/
├── output_0.pdb       # Generated backbone
├── output_1.pdb
├── ...
└── output_99.pdb

Each PDB contains polyalanine backbone - use proteinmpnn for sequence.

Sample output

Successful run

$ python run_inference.py inference.input_pdb=target.pdb contigmap.contigs=[A1-150/0 70-100] inference.num_designs=100
[INFO] Loading model from Complex_base_ckpt.pt
[INFO] Generating design 1/100...
[INFO] Generating design 50/100...
[INFO] Generating design 100/100...
[INFO] Saved 100 designs to output/
 
Generated:
output/output_0.pdb (85 residues)
output/output_1.pdb (92 residues)
...

What good output looks like:

File size: 3-8 KB per PDB (backbone only)
Residue count within specified range
Secondary structure visible in PyMOL (helices/sheets, not random coil)

Decision tree

Should I use RFdiffusion?
│
├─ Need to generate protein backbone?
│  ├─ Yes → Continue below
│  └─ No, already have backbone → Use ProteinMPNN
│
├─ What type of design?
│  ├─ Binder for protein target → RFdiffusion ✓
│  ├─ De novo monomer → RFdiffusion ✓
│  ├─ Motif scaffolding → RFdiffusion ✓
│  └─ Symmetric assembly → RFdiffusion ✓
│
└─ Priority?
   ├─ Need highest success rate → Consider BindCraft
   ├─ Need diversity/exploration → RFdiffusion ✓
   └─ Need all-atom precision → Consider BoltzGen

Typical performance

Campaign Size	Time (A10G)	Cost (Modal)	Notes
100 backbones	20-30 min	~$3	Quick exploration
500 backbones	1.5-2h	~$12	Standard campaign
1000 backbones	3-4h	~$25	Large campaign

Expected downstream yield: ~10-15% of backbones pass full QC after sequence design + validation.

Verify

bash

ls output/*.pdb | wc -l  # Should match num_designs

Troubleshooting

Designs lack secondary structure: Decrease noise_scale to 0.5-0.8 Binder not contacting hotspots: Verify residue numbering, increase num_designs OOM errors: Reduce batch size or use A100 GPU Slow generation: Reduce diffuser.T to 25-35

Error interpretation

Error	Cause	Fix
`RuntimeError: CUDA out of memory`	GPU VRAM exceeded	Use A100 or reduce designs per batch
`KeyError: 'A'`	Chain not found in PDB	Check chain IDs with `grep ^ATOM target.pdb \	cut -c22 \	sort -u`
`ValueError: invalid contig`	Syntax error in contigs	Check for spaces, quotes, commas (see Common Mistakes)
`FileNotFoundError: ckpt`	Missing model weights	Download from IPD website

Next: proteinmpnn for sequence design → structure prediction for validation → protein-qc for filtering.

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Source

SKILL.md in the repositoryCheckingskills/rfdiffusion/SKILL.md

Popularity

Repository stars: 3,013
Repository forks: 410
7-day trend score: 40

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 17 Sep 2026, a month ago.

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