| Name | biosynfoni JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | a *biosynformatic* fingerprint to explore natural product distance and diversity |
| upload_time | 2025-02-06 04:58:46 |
| maintainer | Lucina-May Nollen |
| docs_url | None |
| author | Lucina-May Nollen |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2023 BioSynFoni (biosynfoni)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
bioinformatics
biosynthetic-distance
cheminformatics
metabolites
metabolomics
molecular-fingerprint
natural-products
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<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).
Raw data
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