Metadata-Version: 2.4
Name: currybo_benchmarks
Version: 0.1
Summary: Benchmarking CurryBO for chemical reaction optimization
License-Expression: MIT
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: torch>=2.2.1
Requires-Dist: botorch>=0.10.0
Requires-Dist: gpytorch>=1.11
Requires-Dist: matplotlib>=3.8.3
Requires-Dist: imageio>=2.34.0
Requires-Dist: pandas>=2.2.1
Requires-Dist: rdkit>=2023.9.5
Requires-Dist: gauche>=0.1.6
Requires-Dist: pypalettes
Requires-Dist: pub_ready_plots
Provides-Extra: dev
Requires-Dist: black>=21.12b0; extra == "dev"
Requires-Dist: flake8>=6.1.0; extra == "dev"
Requires-Dist: isort>=5.12.0; extra == "dev"

![CurryBO-logo](https://raw.githubusercontent.com/digital-chemistry-laboratory/currybo/refs/heads/main/CurryBO-logo.svg)
[![License: MIT](https://img.shields.io/badge/License-MIT-red.svg)](./LICENSE)
[![Python Version](https://img.shields.io/badge/Python-3.9%2B-blue?logo=python&logoColor=white)](https://www.python.org/)

[![arXiv](https://img.shields.io/badge/arXiv-2502.18966-b31b1b.svg)](https://arxiv.org/abs/2502.18966)



# Benchmarking for CurryBO: Bayesian optimization over curried function spaces

`CurryBO_benchmarks` is a package that allows to run the chemical reaction optimization benchmarks for the [CurryBO paper](https://arxiv.org/abs/2502.18966), which can be found in the [currybo repository](https://github.com/digital-chemistry-laboratory/currybo).

## Installation

To install the package, simply:
```
pip install currybo-benchmarks
```

## Usage

If you are interested in applying the CurryBO benchmarks for chemical reaction optimization, you can simply call
```
currybo-benchmarks [Denmark/Deoxyfluorination/Cernak/Borylation]
```

Denmark/Deoxyfluronation/Cernak/Borylation are the respective datasets discussed in the paper. Additionally, you can set more arguments for modifying your CurryBO settings, as explained in the [documentation](https://currybo.ethz.ch/docs). 

## Citation

If you use CurryBO in your research, please cite the corresponding paper:

```
@misc{schmid_one_2025,
	title = {One {Set} to {Rule} {Them} {All}: {How} to {Obtain} {General} {Chemical} {Conditions} via {Bayesian} {Optimization} over {Curried} {Functions}},
	url = {http://arxiv.org/abs/2502.18966},
	doi = {10.48550/arXiv.2502.18966},
	publisher = {arXiv},
	author = {Schmid, Stefan P. and Rajaonson, Ella Miray and Ser, Cher Tian and Haddadnia, Mohammad and Leong, Shi Xuan and Aspuru-Guzik, Alán and Kristiadi, Agustinus and Jorner, Kjell and Strieth-Kalthoff, Felix},
	month = feb,
	year = {2025},
	note = {arXiv:2502.18966 [cs]},
}
```
