Data for: hERG Liability Classification Models Using Machine Learning Techniques

Published: 13 May 2019 | Version 1 | DOI: 10.17632/32pdx7y72x.1

Description of this data

This file pertains

  1. all SMILES(except the evaluation set-3 which contains the compound data from in-house proprietary projects) with respective pIC50 values that were used in training and evaluating the models
  2. list of descriptors that were used to build models

Experiment data files

This data is associated with the following publication:

hERG liability classification models using machine learning techniques

Published in: Computational Toxicology

Latest version

  • Version 1

    2019-05-13

    Published: 2019-05-13

    DOI: 10.17632/32pdx7y72x.1

    Cite this dataset

    Kristam, Rajendra; S, Keerthi Praba; Konda, Leela Sarath Kumar (2019), “Data for: hERG Liability Classification Models Using Machine Learning Techniques”, Mendeley Data, v1 http://dx.doi.org/10.17632/32pdx7y72x.1

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Categories

Chemoinformatics, Quantitative Structure-Activity Relationship, Toxicity, Cardiotoxicity, Long QT Syndrome, Computational Chemistry, Modelling

Licence

CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean?

You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

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