| Abstract
| - A set of 538 inhibitors of the tyrosine kinase, Syk, including purines, pyrimidines, indoles, imidazoles,pyrazoles, and quinazolines, has been analyzed using a stepwise nonparametric regression (SNPR) algorithm,which has been developed for QSAR studies of pharmacological data. The algorithm couples stepwisedescriptor selection with flexible, nonparametric, kernel regression, to generate structure−activity relationships.A further 371 molecules have been used as a test set to evaluate the models generated. Descriptors wereselected using an internal monitoring set, and models were assessed using 10% of the principal(538-compound) data set, selected randomly, as an external validation set. The best model had a Q2 of 0.46for the external validation set. Test set predictions were significantly less accurate, partly due to the highermean activity of the test molecules. However at a more coarse-grain level the SNPR models classifiedactive molecules accurately, giving good enrichments. The data sets are difficult to model accurately andSNPR performs better than multilinear regression and a neural network analysis. In the additiveimplementation of SNPR multidimensional models are considered as a sum of single dimensional regressions.This makes the resultant models easily interpretable. For example, in the most predictive SNPR models,there is a clear nonlinear relationship between hydrophobicity (AlogP98) and inhibitory activity.
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