Skip to main content

Package for prediction of chemical species properties from SMILES.

Project description

logo for 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.


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.



pip install

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


✨ 🍰 ✨ TODO


As python module

from tgboost import tgboost.processing.smiles_manager as sm


Contact at


Initial development was supported by AirUCI, Irvine, CA.


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 py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page