tgApp

tgApp is a pipeline framework to be used to develop predictive models of chemical species from SMILES notations. The pipeline comes with a ML model that predicts the Transition glass temperature (T_g) of organic compounds.

Motivation

tgApp is a kickstart project aiming at expanding the use of Machine Learning (ML), Data Engineering and Quantitative Structure–Property Relationships (QSPR) in Physical Chemistry.

Requirements

• Python >=3.6.0 (Python 2.x is not supported)
• NumPy
• pandas
• scikit-learn
• gensim
• RDKit
• mol2vec
• xgboost

Installation

pip install tgApp

Build status

Build status of continus integration i.e. travis, appveyor etc. Ex. -

Documentation

✨ 🍰 ✨ TODO

Usage

As python module

from tgApp import tgApp.processing.smiles_manager as sm

Contribute

Contact at tommaso.galeazzo@gmail.com

Credits

Initial development was supported by AirUCI, Irvine, CA.

License

BSD 3-clause © Tommaso Galeazzo