Data for: Assessment of Several Machine Learning Methods Towards Reliable Prediction of Hormone Receptor Binding Affinity

Published: 22 Jun 2017 | Version 1 | DOI: 10.17632/kn44r3v5p3.1
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Description of this data

PM6 optimised cartesian coordinates of 1589 organic molecules extracted from the EADB.

Experiment data files

This data is associated with the following publication:

Assessment of several machine learning methods towards reliable prediction of hormone receptor binding affinity

Published in: Chemical Data Collections

Latest version

  • Version 1

    2017-06-22

    Published: 2017-06-22

    DOI: 10.17632/kn44r3v5p3.1

    Cite this dataset

    Ho, Junming (2017), “Data for: Assessment of Several Machine Learning Methods Towards Reliable Prediction of Hormone Receptor Binding Affinity”, Mendeley Data, v1 http://dx.doi.org/10.17632/kn44r3v5p3.1

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Categories

Protein-Ligand Interaction, Computational Chemistry

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

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This dataset is 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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