Open AccessDevelopment of a Gaussian Process – feature selection model to characterise (poly)dimethylsiloxane (Silastic ® ) membrane permeation
Author(s) -
Sun Yi,
Hewitt Mark,
Wilkinson Simon C.,
Davey Neil,
Adams Roderick G.,
Gullick Darren R.,
Moss Gary P.
Publication year - 2020
Publication title -
journal of pharmacy and pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.745
H-Index - 118
eISSN - 2042-7158
pISSN - 0022-3573
DOI - 10.1111/jphp.13263
Subject(s) - molecular descriptor , feature selection , computer science , biological system , permeation , artificial intelligence , polydimethylsiloxane , model selection , selection (genetic algorithm) , data set , quantitative structure–activity relationship , machine learning , pattern recognition (psychology) , data mining , chemistry , materials science , membrane , nanotechnology , biochemistry , biology
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.