Inhibition of the tyrosine kinase, Syk, analyzed by stepwise nonparametric regression.
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 stepwise descriptor 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 were selected 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 Q(2) of 0.46 for the external validation set. Test set predictions were significantly less accurate, partly due to the higher mean activity of the test molecules. However at a more coarse-grain level the SNPR models classified active molecules accurately, giving good enrichments. The data sets are difficult to model accurately and SNPR performs better than multilinear regression and a neural network analysis. In the additive implementation 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.
McNeany
T John
TJ
School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
Hirst
Jonathan D
JD
D004791:Enzyme Inhibitors / Q000737:chemistry / Q000494:pharmacology; D011505:Protein-Tyrosine Kinases / Q000037:antagonists & inhibitors*; D021281:Quantitative Structure-Activity Relationship; D018709:Statistics, Nonparametric; D051746:ZAP-70 Protein-Tyrosine Kinase
D004791:Enzyme Inhibitors; D011505:Protein-Tyrosine Kinases; D051746:ZAP-70 Protein-Tyrosine Kinase