semvec/pypi-packages huggingface dataset:
pybatchclassyfire 0.1.6
A python client for batch queries of the ClassyFire API
License: MIT
Keywords: chemoinformatics,ClassyFire

## pybatchclassyfire


This package provides a set of helper functions in python for 
annotating chemical structures with the chemical ontology developed by 
Djoumbou et. al using the [ClassyFire API](http://classyfire.wishartlab.com/)

Such function are available from the pyclassyfire package (https://github.com/JamesJeffryes/pyclassyfire) developped by James Jeffryes and from pyMolNetEnhancer (https://pypi.org/project/pyMolNetEnhancer/) package written by Madeleine Ernst, which relied in part on pyclassyfire.

However the ClassyFire API now only returns paginated JSON files (10 compounds/page) see <http://classyfire.wishartlab.com/access> and tolerates no more than 10 GET request per second.

Building on pyclassyfire and pyMolNetEnhancer functions, pybatchclassyfire as for objective to overcome these limits and allow the batch classification of large tables of structures.



### Table of contents

* [Installation](#installation)
* [Running the batch classification](#running_instructions)
* [Dependencies](#dependencies)
* [Main citation](#main_citation)
* [License](#license)



## Instalation <a name="instalation"></a>

Install pybatchclassyfire with:

 `pip install pybatchclassyfire`

And additional required packages with:

 `pip install -r requirements.txt`


## Running the batch classification <a name="running_instructions"></a>

In order to run the batch classification make sure to have InChI and InChIKey formated chemical structures.
Check [Getting Started with the RDKit in Python](https://www.rdkit.org/docs/GettingStartedInPython.html) for format conversion process.


* After installation of the pybatchclassyfire package, download the corresponding gitlab repository

 `git clone https://gitlab.unige.ch/Pierre-Marie.Allard/pybatchclassyfire.git`

* Make sure to be able to run the Jupyter notebook (pybatchclassyfire_nb.ipynb) on the example file (test_datatable.tsv)

* Adapt Jupyter notebook to treat your file.


## Dependencies <a name="dependencies"></a>

decorator==4.4.2
joblib==0.14.1
networkx==2.4
numpy==1.18.2
pandas==1.0.3
pybatchclassyfire==0.1.3
python-dateutil==2.8.1
pytz==2019.3
requests>=2.6
requests-cache==0.5.2
six==1.14.0



## Main citation <a name="main_citation"></a>

https://gitlab.unige.ch/Pierre-Marie.Allard/pybatchclassyfire.git

## License <a name="license"></a>
This repository is available under the following license https://gitlab.unige.ch/Pierre-Marie.Allard/pybatchclassyfire/-/blob/master/LICENSE.md