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
Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogS ± 0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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: |
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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: | 25 Jun 2023 22:27 |
Status: | Published |
Publisher: | Nature Research |
Identification Number: | 10.1038/s41467-020-19594-z |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166641 |