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.
|Title of host publication||Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010|
|Number of pages||6|
|Publication status||Published - 2010|
|Event||18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010 - Bruges, Belgium|
Duration: 28 Apr 2010 → 30 Apr 2010
|Other||18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010|
|Period||28/04/10 → 30/04/10|