README.md

---
license: bsd-3-clause
language:
- en
tags:
- OneScience
- protein backbone generation
- protein design
frameworks:
- PyTorch
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">RFdiffusion</span>
  </strong>
</p>

# Model Overview

RFdiffusion is a diffusion-based method for protein backbone generation and design. It can be used for unconditional backbone generation, motif scaffolding, PPI/binder design, and symmetric oligomer sampling.

# Model Description

RFdiffusion is a generative protein design model based on the RoseTTAFold three-track network and an SE(3)-equivariant denoising diffusion process. It can progressively generate protein backbones from random structures while satisfying specified topology or functional constraints.

The current Hugging Face package is designed for download-and-use workflows, local quick validation, and OneCode automated runtime scenarios. Code, configurations, example inputs, and weights are all included in the current directory.

# Use Cases

| Use case | Description |
| :---: | :--- |
| Unconditional backbone generation | Takes contig constraints as input and outputs designed backbone PDB files. |
| Motif scaffolding | Takes a PDB file containing the motif and contig constraints as input, and outputs scaffold design results. |
| PPI/binder design | Takes the target structure, hotspot, and contig parameters as input, and outputs candidate binder designs. |
| Symmetric oligomer sampling | Uses symmetry configuration to generate symmetric structure designs. |
| Hugging Face full-package validation | Uses the package layout `config/ modules/ scripts/ examples/ weight/` directly for preflight checks and inference. |

# Usage

## 1. Using OneCode

You can try intelligent one-click AI4S programming through the OneCode online environment:

[Try intelligent one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 2. Manual Installation and Usage

**Hardware Requirements**

- Running on a GPU or DCU is recommended.
- CPU can be used for connectivity checks, but it is relatively slow.
- DCU users need to install DTK in advance. DTK 25.04.2 or later is recommended, or the OneScience-recommended version that matches the current cluster.

**Software Requirements**

For more information about adaptation details, contact liubiao@sugon.com.

**Environment Checks**

- NVIDIA GPU:

```bash
nvidia-smi
```

- Hygon DCU:

```bash
hy-smi
```

## Quick Start

### 1. Install the Runtime Environment

```bash
conda create -n onescience311 python=3.11 -y
conda activate onescience311
pip install onescience[bio] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai
```

If the following code cannot find required libraries at runtime, activate CUDA as shown below.

```bash
source ${ROCM_PATH}/cuda/env.sh
export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
export LD_LIBRARY_PATH="$CONDA_PREFIX/lib/python3.11/site-packages/fastpt/torch/lib:$LD_LIBRARY_PATH"
```

### 2. Download the Model Package and Install the Environment

```bash
hf download --model OneScience-Sugon/RFdiffusion --local-dir ./RFdiffusion
```

### Training Weights

Training weights are already included in the `weights` folder and can be used directly after downloading the model package.

### 3. Run Preflight Checks

Check files and real weights:

```bash
python scripts/preflight.py --strict-weights
```

Check local imports after installing dependencies:

```bash
python scripts/preflight.py --strict-weights --strict-imports
```

Validate only the entry point and Hydra configuration without running sampling:

```bash
RF_DIFFUSION_SMOKE_TEST=1 python scripts/run_inference.py
```

### 4. Run Inference

If execution fails because a `.cache` file is missing, you can create it manually.

Example of unconditional backbone sampling:

```bash
python scripts/run_inference.py \
  'contigmap.contigs=[80-80]' \
  diffuser.T=15 \
  inference.final_step=15 \
  inference.num_designs=1 \
  inference.write_trajectory=False \
  inference.output_prefix=outputs/smoke/design
```

Example of motif scaffolding:

```bash
python scripts/run_inference.py \
  inference.input_pdb=examples/input_pdbs/1YCR.pdb \
  'contigmap.contigs=[10-40/A163-181/10-40]' \
  inference.output_prefix=outputs/motif/design
```

Example of symmetric sampling:

```bash
python scripts/run_inference.py --config-name symmetry \
  diffuser.T=15 \
  inference.final_step=15 \
  inference.output_prefix=outputs/symmetry/c2
```

### 5. Common Environment Variables

```bash
export RF_DIFFUSION_MODEL_DIR=weight
export RF_DIFFUSION_INPUT_PDB=examples/input_pdbs/1qys.pdb
export RF_DIFFUSION_OUTPUT_PREFIX=outputs/design
export RF_DIFFUSION_SCHEDULE_DIR=.cache/schedules
```

# Official OneScience Information

| Platform | OneScience main repository | Skills repository |
| --- | --- | --- |
| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |

# Citation and License

RFdiffusion is released under the BSD open-source license (see the [LICENSE](https://github.com/RosettaCommons/RFdiffusion/blob/main/LICENSE) file) and can be used free of charge for both non-profit and commercial purposes.