Skip to main content

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

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

Uploaded Source

Built Distribution

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

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page