Classification of drugs in absorption classes using the classification and regression trees (CART) methodology

Deconinck, E., Hancock, T., Coomans, D., Massart, D.L., and Vander Heyden, Y. (2005) Classification of drugs in absorption classes using the classification and regression trees (CART) methodology. Journal of Pharmaceutical and Biomedical Analysis, 39 (1-2). pp. 91-103.

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Abstract

Classification and regression trees (CART) were evaluated for their potential use in a quantitative structure-activity relationship (QSAR) context. Models were build using the published absorption values for 141 drug-like molecules as response variable and over 1400 molecular descriptors as potential explanatory variables. Both the role of two- and three-dimensional descriptors and their relative importance were evaluated.

For the used dataset, CART models showed high descriptive and predictive abilities. The predictive abilities were evaluated based on both cross-validation and an external test set. Application of the variable ranking method to the models showed high importances for the n-octanol/water partition coefficient (log P) and polar surface area (PSA). This shows that CART is capable of selecting the most important descriptors, as known from the literature, for the absorption process in the intestinal tract.

Item ID: 4551
Item Type: Article (Refereed Research - C1)
Keywords: absorption classes; CART; classification and regression trees
ISSN: 1873-264X
Date Deposited: 18 Sep 2009 02:16
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 50%
03 CHEMICAL SCIENCES > 0301 Analytical Chemistry > 030106 Quality Assurance, Chemometrics, Traceability and Metrological Chemistry @ 50%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
Citation Count from Web of Science Web of Science 51
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