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Package for prediction of chemical species properties from SMILES.

Project description

logo for tgboost

tgBoost

tgBoost 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 glasss transition temperature (Tg) of organic compounds.

Motivation

tgBoost 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

Installation

pip install https://github.com/U0M0Z/tgboost

The tgBoost library needs the independent installation of rdkit via conda-forge:

conda install -c conda-forge rdkit

Build status

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

Build Status Windows Build Status

Documentation

✨ 🍰 ✨ TODO

Usage

As python module

from tgboost import tgboost.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

Project details


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tgapp-0.0.1.tar.gz (57.7 MB view hashes)

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tgapp-0.0.1-py3-none-any.whl (57.4 MB view hashes)

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