The application of gaussian processes in the prediction of percutaneous absorption for mammalian and synthetic membranes

Yi Sun, Gary P. Moss, Maria Prapopoulou, Rod Adams, Marc B. Brown, Neil Davey

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Improving prediction of the skin permeability coefficient is a difficult problem, and an important issue with the increasing use of skin patches as a means of drug delivery. In this work, we apply Gaussian Processes (GPs) with five different covariance functions to predict the permeability coefficients of human, pig, rodent and silastic membranes. We obtain a considerable improvement over quantitative structure-activity relationship (QSARs) predictors. The GPs with Mat́ern and neural network covariance functions give the best performance in this work. We find that five compound features applied to human, pig and rodent membranes cannot represent the main characteristics of the silastic dataset.

Original languageEnglish
Title of host publicationProceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Pages457-462
Number of pages6
Publication statusPublished - 2010
Event18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010 - Bruges, Belgium
Duration: 28 Apr 201030 Apr 2010

Other

Other18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010
Country/TerritoryBelgium
CityBruges
Period28/04/1030/04/10

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