Metadata-Version: 2.1
Name: enzeptional
Version: 1.0.3
Summary: Enzeptional stand-alone python package.
Author: GT4SD team
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: transformers <5.0.0,>=4.22.0
Requires-Dist: torch <=1.12.1,>=1.0
Requires-Dist: numpy <1.24.0,>=1.16.5
Requires-Dist: pandas <=2.0.3
Requires-Dist: joblib >=1.1.0
Requires-Dist: xgboost >=1.7.6
Requires-Dist: importlib-resources >=5.10.0
Requires-Dist: tape-proteins >=0.4
Requires-Dist: scikit-learn <1.3.0,>=1.0.0
Provides-Extra: dev
Requires-Dist: black ; extra == 'dev'
Requires-Dist: isort ; extra == 'dev'
Requires-Dist: mypy ; extra == 'dev'
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'

# Enzyme Optimization of Biocatalytic Reactions

This repository provides an example on how ro run the framework for the optimization of enzymes within the context of biocatalytic reactions.


### Development setup & installation

Create any virtual or conda environment compatible with the specs in setup.cfg. Then run:
```sh
pip install -e ".[dev]" 
```


### Running the example

To run the example simply type:

```sh
python example_enzeptional.py
```


### References

If you use `Enzeptional` in your projects, please consider citing the following:

```bibtex
@inproceedings{teukam2023enzyme,
  title={Enzyme optimization via a generative language modeling-based evolutionary algorithm},
  author={Teukam, Yves Gaetan Nana and Grisoni, Francesca and Manica, Matteo and Zipoli, Federico and Laino, Teodoro},
  booktitle={American Chemical Society (ACS) Spring Meeting},
  year={2023}
}
```

### License

The `Enzeptional` codebase is under MIT license.
For individual model usage, please refer to the model licenses found in the original packages.
