Prediction of Skin Penetration Using Machine Learning Methods

Improving predictions of the skin permeability coefficient is a difficult problem. It is also an important issue with the increasing use of skin patches as a means of drug delivery. In this work, we apply K-nearest-neighbour regression, single layer networks, mixture of experts and Gaussian processes to predict the permeability coefficient. We obtain a considerable improvement over the quantitative structureactivity relationship (QSARs) predictors. We show that using five features, which are molecular weight, solubility parameter, lipophilicity, the number of hydrogen bonding acceptor and donor groups, can produce better predictions than the one using only lipophilicity and the molecular weight. The Gaussian process regression with five compound features gives the best performance in this work.

• Type: Conference
• Year: 2008
• Where: ICDM
• Authors: Yi Sun, Gary P. Moss, Maria Prapopoulou, Rod Adams, Marc B. Brown, Neil Davey
• Keywords: Data Mining, ICDM 2008, Molecular Weight, Permeability Coefficient, Skin Permeability Coefficient