The influence of solid state information and descriptor selection on statistical models of temperature dependent aqueous solubility.

Marchese Robinson, RL orcid.org/0000-0001-7648-8645, Roberts, KJ orcid.org/0000-0002-1070-7435 and Martin, EB (2018) The influence of solid state information and descriptor selection on statistical models of temperature dependent aqueous solubility. Journal of Cheminformatics, 10. 44.

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Copyright, Publisher and Additional Information: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Keywords: Quantitative structure–property relationships; Solubility; Temperature dependent solubility data; Enthalpy of solution; Machine learning; Random forest; Multiple linear regression; Feature selection; Crystal structure; Lattice energy; Melting point
Dates:
  • Published: December 2018
  • Accepted: 17 August 2018
  • Published (online): 29 August 2018
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering (Leeds) > School of Chemical & Process Engineering (Leeds)
Funding Information:
FunderGrant number
Innovate UK fka Technology Strategy Board (TSB)14060
Depositing User: Symplectic Publications
Date Deposited: 04 Sep 2018 12:40
Last Modified: 04 Sep 2018 13:20
Status: Published
Publisher: Springer Nature
Identification Number: https://doi.org/10.1186/s13321-018-0298-3
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