{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "rosettacommons-rfdiffusion--design-a-binder-to-a-target-with-rfdiffusion",
 "repo": "RosettaCommons/RFdiffusion",
 "repo_url": "https://github.com/RosettaCommons/RFdiffusion",
 "repo_slug": "rosettacommons-rfdiffusion",
 "title": "Design a binder against a target protein",
 "page_title": "How to design a binder against a target protein with RFdiffusion",
 "summary": "Diffuse a binder against a target chain, using hotspot residues to aim it at the right surface.",
 "who": "A team looking for de novo binders to a receptor or antigen.",
 "problem": "Binder campaigns are slow and the model will otherwise bind the wrong patch.",
 "outcome": "A set of candidate binder backbones aimed at the interface you chose.",
 "steps": [
  "Set up the repo and the SE3nv environment as in the installation section",
  "Run `./scripts/run_inference.py 'contigmap.contigs=[B1-100/0 100-100]' inference.output_prefix=test_outputs/binder_test inference.num_designs=10` with your target as chain B",
  "Aim the binder at the interface with hotspot residues, specified as `'ppi.hotspot_res=[A30,A33,A34]'`",
  "For more diverse topologies try the beta model with `inference.ckpt_override_path=models/Complex_beta_ckpt.pt`, and see ./examples/design_ppi.sh"
 ],
 "agent_prompt": "With RosettaCommons/RFdiffusion, design 10 binders to residues 1-100 of chain B in <my target pdb> with hotspot residues <my hotspots>, and explain how to pick the hotspots from the structure.",
 "agents": [
  "claude-code",
  "codex",
  "cursor",
  "gemini-cli"
 ],
 "difficulty": "Advanced",
 "time": "4 hours",
 "basis": "readme",
 "caveat": "The beta model generates more topologies but the README says it has not been extensively experimentally validated.",
 "category": "Security and science",
 "tags": [
  "Security and science"
 ],
 "lang": "",
 "requirements": [
  "A clone of the repo plus the model weights downloaded into its models folder",
  "Anaconda or Miniconda, and NVIDIA's SE(3)-Transformer installed from the env folder",
  "An NVIDIA GPU; the provided environment file targets CUDA 11.1 and may need adapting",
  "Docker as an alternative to the conda setup, using the provided docker/Dockerfile"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "platform",
   "notice": "Diffusion is computationally intensive, so a large uncropped target makes runs very slow."
  }
 ],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/rosettacommons-rfdiffusion/design-a-binder-to-a-target-with-rfdiffusion/",
 "md": "https://dedupedai.com/use-cases/rosettacommons-rfdiffusion/design-a-binder-to-a-target-with-rfdiffusion/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/rosettacommons-rfdiffusion--design-a-binder-to-a-target-with-rfdiffusion.json",
 "api_version": "v1"
}