RosettaCommons / RFD4-Proteina

A flow-matching foundation model for biomolecular design — Baker Lab × NVIDIA GenAIR 
 Project page ·
 Paper ·
 Try it now ·
 Requirements ·
 Installation ·
 Quick start ·
 Workflows ·
 Tutorials ·
 Citation ·
 Documentation 
## Try it now
No install needed: design in your browser with the Hugging Face Studio Spaces.
- RFD4-Proteina Studio (ZeroGPU): runs on your own ZeroGPU quota.
- RFD4-Proteina Studio (dedicated GPUs): a shared dedicated GPU pool.
Both require a Hugging Face sign-in, and both screen designs with SecureDNA.
RFD4-Proteina designs proteins and biomolecular complexes with sequence–structure co-design,
motif and binding-site conditioning, partial diffusion, and reward-guided search.
Key features:
- **Many design tasks from one model:** monomers, protein and DNA binders, antibodies and nanobodies, enzymes and motif scaffolding, small-molecule pockets.
- **Rich conditioning:** annotated CIF inputs or text-based YAML specifications (the quickest way to start) for fixed motifs, hotspots, burial, hydrogen bonds, and more.
- **Reward-guided search:** beam search over generation paths scored by structure-prediction oracles and custom metrics.
- **Partial diffusion** to refine or diversify existing structures.
- **Fast inference:** `torch.compile` by default, with an optional BioIR backend (`inference_backend=bioir`).
- **Hugging Face release and Python API** alongside the `rfd4-generate`, `rfd4-evaluate` and training CLIs.
## System requirements
- Linux x86_64, Python 3.12, and an NVIDIA GPU with a CUDA 13-compatible driver (CUDA 12 and CPU-only containers are also available).
- Tested GPUs: see the supported hardware table. Check yours with `nvidia-smi`.
- macOS and Windows are not supported.
## Install
**Start with `make setup`.** On a machine that meets the requirements above:
 [code] 
The repository history is large because of past notebook outputs. For a fast clone use
`git clone --depth 1` (no history) or `git clone --filter=blob:none` (full history, blobs fetched on demand).
Notebooks are committed without outputs; run `pre-commit install` to enforce this with `nbstripout`.
Setup installs the environment, caches the released Hugging Face model bundle
(about 1.7 GB, including its autoencoder), and configures `DATA_PATH` for optional data.
The cache location follows `HF_HOME`. See the
installation guide
for CUDA build prerequisites, optional metrics, and cluster setup.
Setup also asks whether to add an AlphaFold 2 or AlphaFold 3 / OpenFold3 environment for
reward-guided search and refolding metrics (and, if so, your CPU architecture and CUDA version).
Skip that with `bash setup.sh --skip-search-envs`; run it later with `bash scripts/setup_search_envs.sh`.
Other options (CUDA 12, CPU-only, Docker, Apptainer, pip) are listed in the
deployment options.
For example, a container with CUDA 13 (use `RFD4_ACCELERATOR=cu12` or `cpu` for the others):
 [code] 
If access to the Hugging Face repositories is restricted, supply `HF_TOKEN` or use `h