Metadata-Version: 2.4
Name: Thermopred
Version: 1.0.0
Summary: A python package to predict some thermochemical properties.
Home-page: https://github.com/jeffrichardchemistry/thermopred
Author: Jefferson Richard; Diullio P
Author-email: jrichardquimica@gmail.com
License: GNU GPL
Keywords: Cheminformatics,Chemistry,QSAR,QSPR,Fingerprint,Spectroscopy
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Environment :: MacOS X
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: scikit-learn==1.3.2
Requires-Dist: pandas<=2.1.4
Requires-Dist: numpy<=1.26.4
Requires-Dist: rdkit==2023.9.6
Requires-Dist: xgboost==2.0.3
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: license-file
Dynamic: requires-dist
Dynamic: summary

# Thermopred
This repository contains the official data, algorithms, and ML models present in the paper "`AI-Enhanced Quantum Chemistry Dataset for Thermochemical Properties of API-Like Compounds and Their Degradants`".

## How to use

Download the repository manually or via git:

```shell
$ git clone https://github.com/jeffrichardchemistry/thermopred
```

Enter the `thermopred` directory and run the following command to install the python package:

```shell
$ cd thermopred

$ python3 setup.py install
```

Once this is done, it is now possible to use the package by simply importing the modules. Import the modules as described below and pass a smiles for prediction.

```python
from Thermopred.Enthalpie import EnthalpieEnergy
from Thermopred.GibbsEnergy import GibbsFreeEnergy

smiles='CN1C=CN(CCCN(c2cc(Cl)ccc2O)c2ccccc2S)CC1'

ee = EnthalpieEnergy()
result_enthalpie = ee.predict(smiles)

gfe = GibbsFreeEnergy()
gfe.predict(smiles=smiles)
```
