rfdiffusion

name	rfdiffusion
description	Generate de novo protein backbones with RFdiffusion for protein-target binders, hotspot-conditioned interfaces, motif scaffolding, partial diffusion, or symmetric assemblies. Use this skill when a design workflow needs reproducible RFdiffusion contigs, residue mappings, checkpoints, seeds, batch execution, and handoff to ProteinMPNN plus independent structure validation. RFdiffusion generates backbones; it does not validate folding or binding.
license	MIT
origin	openai4s
category	biomodels
requirements	["gpu"]
capabilities	{"network":{"mode":"raw_required","domains":[]}}
metadata	{"display-name":"RFdiffusion","third_party":[{"kind":"recipe","name":"Adaptyv protein-design-skills RFdiffusion recipe","license":"MIT","terms_url":"https://github.com/adaptyvbio/protein-design-skills/blob/b231232025e3db41e9c637abc1e8082e915f6cb1/LICENSE"},{"kind":"code-and-weights","name":"RFdiffusion","license":"BSD-3-Clause","terms_url":"https://github.com/RosettaCommons/RFdiffusion/blob/main/LICENSE"}]}

RFdiffusion

Use RFdiffusion for the backbone-generation stage of a protein-design workflow. Treat its output as a structural proposal, not as evidence that a sequence folds or binds. Design sequences afterward with proteinmpnn (or ligandmpnn when non-protein atoms must be visible), then validate both the isolated design and the target–design complex with an independent predictor such as boltz, chai1, or alphafold2.

The adapted recipe is MIT-licensed. The upstream RFdiffusion code and the model weights referenced by its README are BSD-3-Clause; record the exact upstream commit and checkpoint digest used by each run.

Preflight: freeze the design contract

Before inference:

1. Preserve the original chain IDs and residue numbers. Write an explicit map if the input is renumbered, cropped, or has insertion codes.
2. Audit missing residues/atoms, alternate locations, non-standard residues, and biological assembly choice. Do not silently remove cofactors or chains.
3. For binder design, confirm proposed hotspot residues are surface-accessible and belong to the intended target chain. A typical pilot uses 3–6 spatially coherent hotspots, but the biological interface determines the final set.
4. Freeze the target PDB digest, RFdiffusion commit, checkpoint digest, contig string, hotspot list, seed policy, number of designs, and output prefix in a machine-readable manifest.
5. Keep target-only coordinates separate from any withheld reference binder. Do not leak a reference complex into generation or validation.

Do not trim a target merely to make inference cheaper unless the retained construct is biologically justified and the residue map is preserved.

Install and invoke the official runner

RFdiffusion is a source repository rather than an OpenAI4S sidecar. Prepare a pinned GPU environment, clone a fixed revision of the official repository, install it there, and download the documented checkpoint. Verify every download before starting a campaign. Follow the upstream CUDA/PyTorch/DGL compatibility instructions for the selected revision; do not improvise a version matrix from this recipe.

Do not install RFdiffusion into OpenAI4S's shared struct environment. That environment intentionally targets portable Python 3.13 with a CPU PyTorch build, whereas upstream RFdiffusion publishes a Python 3.9, CUDA-specific PyTorch/DGL stack. Use a pinned dedicated conda environment or the official RFdiffusion Docker image pinned by digest. openai4s setup --profile full creates the shared struct environment; it does not provision RFdiffusion, GPU drivers, model weights, or a container image.

Run scripts from the RFdiffusion repository root. The official inference entry point is :