Metadata-Version: 2.4
Name: biosynfoni
Version: 1.0.0
Summary: a *biosynformatic* fingerprint to explore natural product distance and diversity
Project-URL: homepage, https://github.com/lucinamay/biosynfoni
Author: Lucina-May Nollen
Maintainer: Lucina-May Nollen
License: MIT License
        
        Copyright (c) 2023 BioSynFoni (biosynfoni)
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
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        The above copyright notice and this permission notice shall be included in all
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License-File: LICENSE
Keywords: bioinformatics,biosynthetic-distance,cheminformatics,metabolites,metabolomics,molecular-fingerprint,natural-products
Requires-Python: >=3.9
Requires-Dist: numpy
Requires-Dist: rdkit
Requires-Dist: tqdm
Provides-Extra: dev
Requires-Dist: black; extra == 'dev'
Requires-Dist: black[jupyter]; extra == 'dev'
Requires-Dist: pytest; extra == 'dev'
Provides-Extra: experiments
Requires-Dist: jupyter; extra == 'experiments'
Requires-Dist: matplotlib; extra == 'experiments'
Requires-Dist: networkx; extra == 'experiments'
Requires-Dist: pandas; extra == 'experiments'
Requires-Dist: requests; extra == 'experiments'
Requires-Dist: scikit-learn>=1.3.0; extra == 'experiments'
Requires-Dist: scipy; extra == 'experiments'
Requires-Dist: seaborn; extra == 'experiments'
Requires-Dist: umap-learn; extra == 'experiments'
Provides-Extra: test
Requires-Dist: matplotlib; extra == 'test'
Description-Content-Type: text/markdown

<img width="800" alt="스크린샷 2023-10-19 오후 7 59 27" src="https://github.com/lucinamay/biosynfoni/assets/119406697/c2b32601-8a00-4520-b027-101206becf81">\
<span style="color:green"> 🌿 *a biosynformatic molecular fingerprint tailored to natural product chem- and bioinformatic research* 🌿</span>


<p align="center">
    <a href="https://github.com/lucinamay/biosynfoni/actions/workflows/test-biosynfoni.yml">
        <img alt="Tests" src="https://github.com/lucinamay/biosynfoni/actions/workflows/test-biosynfoni.yml/badge.svg" /></a>
    <a href="https://pypi.org/project/biosynfoni">
        <img alt="PyPI" src="https://img.shields.io/pypi/v/biosynfoni" /></a>
    <a href="https://pypi.org/project/biosynfoni">
        <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/biosynfoni" /></a>
    <a href="https://github.com/lucinamay/biosynfoni/blob/main/LICENSE">
        <img alt="PyPI - License" src="https://img.shields.io/pypi/l/cinemol" /></a>
    <a href='https://github.com/psf/black'>
        <img src='https://img.shields.io/badge/code%20style-black-000000.svg' alt='Code style: black' /></a>
    <a href="https://doi.org/10.5281/zenodo.14822624">
        <img src="https://zenodo.org/badge/DOI/10.5281/zenodo.14822624.svg" alt="DOI"></a>
    <a href="https://fairsoftwarechecklist.net/v0.2?f=20&a=30112&i=20122&r=123">
        <img src="https://fairsoftwarechecklist.net/badge.svg" alt="FAIR checklist badge"></a>
</p>

\________________________________________________________________________________________


  **bi·o·syn·for·ma·tic**\
  /ˌbaɪ  oʊ  sɪn  fərˈ mæt ɪk/\
  *adjective Computers, Biochemistry*

  relating to biosynthetic information and biochemical logic.\
  as a concatenation of  *biosynthetic* and *bioinformatics*, it was coined\
  during the creation of `BioSynFoni`.

\_________________________________________________________________________________________


### Getting started 🌿

#### Predict biosynthetic class

We have trained a biosynthetic class predictor on `biosynfoni` fingerprints. 

You can try out the predictor on your own molecules [here](https://moltools.bioinformatics.nl/biosynfoni)!

#### Installation

Biosynfoni requires Python 3.9 or later. RDKit is installed as a dependency when installing Biosynfoni.

To install the package, you can use pip:

```bash
pip install biosynfoni
```

Now you can import the `biosynfoni` package in your Python code or use the command line tool.

#### Usage in Python

Convert a SMILES string to a fingerprint:

```python
from biosynfoni import Biosynfoni
from rdkit import Chem

smi = <SMILES>
mol = Chem.MolFromSmiles(smi)
fp = Biosynfoni(mol).fingerprint  # returns biosynfoni's count fingerprint of the molecule
```

#### Usage in the command line

Create a fingerprint from a SMILES string:

```bash 
biosynfoni <SMILES>
```

Create a fingerprint from an InChI string:

```bash
biosynfoni <InChI>
```

Write the fingerprints of all molecules in an SDF file to a CSV file:

```bash
biosynfoni <molecule_supplier.sdf>
```

<!-- ### Preprint

#### Citation

If you use `biosynfoni` in your research, please cite our [preprint](https://chemrxiv.org/engage/chemrxiv/public-dashboard):

```bibtex
@article{nollen2025biosynfoni,
  title={Biosynfoni: A Biosynthesis-informed and Interpretable Lightweight Molecular Fingerprint},
  author={Nollen, Lucina-May, Meijer, David, Sorokina, Maria, and Van der Hooft, Justin J. J.},
  journal={chemRxiv},
  year={2025}
}
``` -->

#### Data availability

We created several biosynthetic class predictors for our manuscript, which can be downloaded from Zenodo [here](https://zenodo.org/records/14791239).

We have used data from the [COCONUT](https://coconut.naturalproducts.net) natural product database ([DOI](https://doi.org/10.1186/s13321-020-00478-9)) and [ZINC](https://zinc.docking.org) compound database ([DOI](https://pubs.acs.org/doi/10.1021/acs.jcim.0c00675)). The parsed data used for the analysis in our manuscript can be downloaded from Zenodo [here](https://zenodo.org/records/14791205). 



