Classification of Membrane Permeability of Drug Candidates: A Methodological Investigation

• Published in: QSAR & combinatorial science Vol. 24; no. 4; pp. 449 - 457
• Main Authors: Jensen, Berith F., Refsgaard, Hanne H. F., Bro, Rasmus, Brockhoff, Per B.
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY-VCH Verlag 01.06.2005; WILEY‐VCH Verlag
• Subjects: classification; in silico prediction; Permeability; weighted classification accuracy
• ISSN: 1611-020X; 1611-0218
• DOI: 10.1002/qsar.200430928

A data set consisting of 1040 drug candidates was divided into a training set and test set of 832 and 208 compounds, respectively. The training set was used for estimating a model for classification into two classes with respect to membrane permeation in a cell based assay: 1) apparent permeability below 4 * 10−6 cm/s and 2) apparent permeability on 4 * 10−6 cm/s or higher. Nine molecular descriptors were calculated for each compound and six classification techniques were applied: k‐Nearest Neighbor, Linear and Quadratic Discriminant Analysis, Discriminant Adaptive Nearest‐Neigbor, Soft Independent Modeling of Class Analogy and Classification Tree. A Discriminant Adaptive Nearest‐Neigbor model based on four descriptors: Number of flex bonds, number of hydrogen bond donors, molecular weight and molecular polar surface area was selected as the best model. The selection was based on cross validation and a new weighted classification accuracy measure introduced in this study. In the test set of 208 compounds 9% was not classified. The false positive rate was 0.08 and the sensitivity was 0.76.