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Official open-source of CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model

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###  CarbonNovo
CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model. M. Ren, T. Zhu, H. Zhang. ICML 2024. <https://proceedings.mlr.press/v235/ren24e.html>

*If you encounter any issues with the installation or would like to report a bug, please feel free to open an issue on GitHub at <https://github.com/CarbonMatrixLab/carbonnovo/issues>*.

### Installation
```bash
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia

pip install -r requirements.txt

```
**Model weights**
1. Download CarbonNovo model weights from <https://carbonnovo.s3.amazonaws.com/params.tar>, and place them in the ./params directory.
2. Download the ESM2 model weights from <https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t36_3B_UR50D.pt> and <https://dl.fbaipublicfiles.com/fair-esm/regression/esm2_t36_3B_UR50D-contact-regression.pt>, and place them in the `./params` directory.

This is a simple, runnable version. We will improve the code and upload the full version shortly.

### Usage
Example:
```bash
python predict.py sample_length=256 sample_number=4
```
Here, sample_length denotes the length of the proteins to be sampled, and sample_number denotes the number of samples. The sampled structures and sequences will be put in the directory./output.

carbonnovo

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