The application of feature selection to the development of Gaussian process models for percutaneous absorption

The Journal of Pharmacy and Pharmacology
Lun Tak LamGary P Moss

Abstract

The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determ...Continue Reading

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Citations

Nov 15, 2019·The Journal of Pharmacy and Pharmacology·Parivash AshrafiGary P Moss
Jan 12, 2016·The Journal of Pharmacy and Pharmacology·Alpa ShahGary P Moss
Mar 19, 2015·SAR and QSAR in Environmental Research·P AshrafiY Sun
Jan 18, 2018·The Journal of Pharmacy and Pharmacology·Parivash AshrafiGary Patrick Moss
Jan 19, 2019·Journal of Chemical Information and Modeling·Beatrice PecoraroMatthew Traynor

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