Machine Learning with Physicochemical Relationships: Solubility Prediction in Organic Solvents and Water

Boobier, S orcid.org/0000-0002-3166-2782, Hose, DRJ, Blacker, AJ orcid.org/0000-0003-4898-2712 et al. (1 more author) (2020) Machine Learning with Physicochemical Relationships: Solubility Prediction in Organic Solvents and Water. Nature Communications, 11 (1). 5753. ISSN 2041-1723

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Cheminformatics; Computational chemistry; Computational science; Statistics
Dates:
  • Published: December 2020
  • Accepted: 12 October 2020
  • Published (online): 13 November 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemistry (Leeds) > Organic Chemistry (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 14 Oct 2020 12:56
Last Modified: 18 Dec 2020 12:16
Status: Published
Publisher: Nature Research
Identification Number: https://doi.org/10.1038/s41467-020-19594-z
Related URLs:

Share / Export

Statistics