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

Ashrafi, P.; Moss, G.P.; Wilkinson, S.C.; Davey, N.; Sun, Y.

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.

Citation

Ashrafi, P., Moss, G., Wilkinson, S., Davey, N., & Sun, Y. (2015). The application of machine learning to the modelling of percutaneous absorption: An overview and guide. SAR and QSAR in Environmental Research, 26(3), 181-204. https://doi.org/10.1080/1062936x.2015.1018941

• Journal Article Type: Article
• Acceptance Date: Jan 21, 2015
• Online Publication Date: Mar 18, 2015
• Publication Date: Mar 4, 2015
• Deposit Date: Jun 13, 2023
• Journal: SAR and QSAR in Environmental Research
• Print ISSN: 1062-936X
• Electronic ISSN: 1029-046X
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Issue: 3
• Pages: 181-204
• DOI: https://doi.org/10.1080/1062936x.2015.1018941
• Keywords: Drug Discovery; Molecular Medicine; General Medicine; Bioengineering; Gaussian process; machine learning; quantitative structure–permeability relationships (QSPRs)skin permeation; percutaneous absorption
• Additional Information: Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=gsar20