https://github.com/deepgraphlearning/diffpack

Implementation of DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
https://github.com/deepgraphlearning/diffpack

deep-learning diffusion-models molecule protein-structure score-based-generative-modeling score-matching

Last synced: 4 months ago
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Implementation of DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing

• Host: GitHub
• URL: https://github.com/deepgraphlearning/diffpack
• Owner: DeepGraphLearning
• License: mit
• Created: 2023-07-18T18:45:55.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-04T03:55:16.000Z (about 1 year ago)
• Last Synced: 2024-10-09T22:05:45.929Z (4 months ago)
• Topics: deep-learning, diffusion-models, molecule, protein-structure, score-based-generative-modeling, score-matching
• Language: Python
• Homepage: https://arxiv.org/abs/2306.01794
• Size: 10.4 MB
• Stars: 71
• Watchers: 7
• Forks: 6
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing

**DiffPack** is a novel torsional diffusion model designed for predicting the conformation of protein side-chains based on their backbones, as introduced in [arxiv link](https://arxiv.org/abs/2306.01794). By learning the joint distribution of side-chain torsional angles through a process of diffusing and denoising on the torsional space, DiffPack significantly improves angle accuracy across various benchmarks for protein side-chain packing. 

## Installation

You can install DiffPack with the following commands, which will install all the dependencies.

```shell

conda create -n diffpack python=3.8

conda activate diffpack

```

```shell

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

conda install pyg -c pyg

conda install torchdrug -c milagraph -c conda-forge -c pytorch -c pyg

```

```shell

pip install biopython==1.77

pip install pyyaml

pip install easydict

```

![framwork](asset/diffpack.png)

## Model Checkpoints

We provide several versions of DiffPack, each with its own configuration and checkpoint:

| Model                                 | Config                                     | Checkpoint            |

|---------------------------------------|--------------------------------------------|-----------------------|

| DiffPack (Vanila)                     | [Config](config/inference.yaml)            | [Google Drive Link](https://drive.google.com/file/d/1tZ9ZOjIxq9SxrkdvbLJyLUBbt2P-mksO/view?usp=sharing) |

 | DiffPack (with Confidence Prediction) | [Config](config/inference_confidence.yaml) | [Google Drive Link](https://drive.google.com/file/d/1tZ9ZOjIxq9SxrkdvbLJyLUBbt2P-mksO/view?usp=sharing) |

The Vanilla version of DiffPack is the base model, 

while the version with Confidence Prediction includes an additional feature that estimates the confidence score of the predicted side-chain conformation.

Most of the configuration is specified in the configuration file. We list some important configuration hyperparameters here:

- `mode`: Backward mode in diffusion process. We use `ode` or `sde` for DiffPack.

- `annealed_temp`: Annealing temperature in diffusion process. We use `3` for DiffPack. Ideally, higher value corresponds to lower temperature.

- `num_sample`: Number of samples in diffusion process. Confidence model will decide which sample to use.

## Running DiffPack

To use DiffPack for new proteins on your local machine, we provide the necessary configuration files in the `config` folder. 

For instance, if you have two pdb files `1a3a.pdb` and `1a3b.pdb`, 

you can run the following command to infer new proteins and save the results in your chosen output folder:

```shell

python script/inference.py -c config/inference_confidence.yaml \

                           --seed 2023 \

                           --output_dir path/to/output \

                           --pdb_files 1a3a.pdb 1a3b.pdb ...

```

This command will generate and save the predicted side-chain conformations for the given proteins. 

## Retraining DiffPack

For those interested in training DiffPack on their own datasets, we will soon release the code and instructions for this process. 

Stay tuned for updates!

## Visualization of Results

![Visualization](asset/result.png)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Citation

If you find DiffPack useful in your research or project, please cite our paper:

```

@article{zhang2023diffpack,

  title={DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing},

  author={Zhang, Yangtian and Zhang, Zuobai and Zhong, Bozitao and Misra, Sanchit and Tang, Jian},

  journal={arXiv preprint arXiv:2306.01794},

  year={2023}

}

```