The application of machine learning to the modelling of percutaneous absorption: an overview and guide

SAR and QSAR in Environmental Research
P AshrafiY Sun

Abstract

Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This paper reviews the application of these methods to the problem domain of skin permeability and addresses critically some of the key issues. Specifically, ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. However, they are beset by perceptions of a lack of transparency and, often, once a ML or related method has been published there is little impetus from other researchers to adopt such methods. This is usually due to the lack of transparency in some methods and the lack of availability of specific coding for running advanced ML methods. This paper reviews critically the application of ML methods to percutaneous absorption and addresses the key issue of transparency by describing in detail - and providing the detailed coding for - the process of running a ML method (in this case, a Gaussian process regression method). Although this method is applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.

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Citations

Jan 12, 2016·The Journal of Pharmacy and Pharmacology·Alpa ShahGary P Moss
Jul 28, 2016·European Journal of Pharmaceutics and Biopharmaceutics : Official Journal of Arbeitsgemeinschaft Für Pharmazeutische Verfahrenstechnik E.V·Rania M Hathout, Abdelkader A Metwally
Nov 15, 2019·The Journal of Pharmacy and Pharmacology·Parivash AshrafiGary P Moss
Mar 5, 2019·PloS One·Lea HelmersKlaus-Robert Müller
Oct 17, 2019·PloS One·Caleb PomeroySlava Jankin Mikhaylov
Jan 18, 2018·The Journal of Pharmacy and Pharmacology·Parivash AshrafiGary Patrick Moss

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