Prediction of skin penetration using machine learning methods

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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 applyK-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.

Original languageEnglish
Title of host publicationProceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Pages1049-1054
Number of pages6
DOIs
Publication statusPublished - 2008
Event8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italy
Duration: 15 Dec 200819 Dec 2008

Other

Other8th IEEE International Conference on Data Mining, ICDM 2008
CountryItaly
CityPisa
Period15/12/0819/12/08

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  • Cite this

    Sun, Y., Moss, G. P., Prapopoulou, M., Adams, R., Brown, M. B., & Davey, N. (2008). Prediction of skin penetration using machine learning methods. In Proceedings - 8th IEEE International Conference on Data Mining, ICDM 2008 (pp. 1049-1054). [4781223] https://doi.org/10.1109/ICDM.2008.97