Sun, Y, Hewitt, M, Wilkinson, SC, Davey, N, Adams, RG, Gullick, DR and Moss, GPJ (2020) Development of a Gaussian Process - feature selection model to characterise (poly)dimethylsiloxane (Silastic (R)) membrane permeation. Journal of Pharmacy and Pharmacology.

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Abstract

Objectives The current study aims to determine the effect of physicochemical descriptor selection on models of polydimethylsiloxane permeation. Methods A total of 2942 descriptors were calculated for a data set of 77 chemicals. Data were processed to remove redundancy, single values, imbalanced and highly correlated data, yielding 1363 relevant descriptors. For four independent test sets, feature selection methods were applied and modelled via a variety of Machine Learning methods. Key findings Two sets of molecular descriptors which can provide improved predictions, compared to existing models, have been identified. Best permeation predictions were found with Gaussian Process methods. The molecular descriptors describe lipophilicity, partial charge and hydrogen bonding as key determinants of PDMS permeation. Conclusions This study highlights important considerations in the development of relevant models and in the construction and use of the data sets used in such studies, particularly that highly correlated descriptors should be removed from data sets. Predictive models are improved by the methodology adopted in this study, notably the systematic evaluation of descriptors, rather than simply using any and all available descriptors, often based empirically on in vitro experiments. Such findings also have clear relevance to a number of other fields.

Item Type: Article
Additional Information: © 2020 Royal Pharmaceutical Society, Journal of Pharmacy and Pharmacology This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: data set design; feature selection; Gaussian Process Regression; machine learning; polydimethylsiloxane
Subjects: Q Science > Q Science (General)
R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > School of Pharmacy and Bioengineering
Depositing User: Symplectic
Date Deposited: 30 Apr 2020 08:31
Last Modified: 30 Apr 2020 08:31
URI: http://eprints.keele.ac.uk/id/eprint/7911

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