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Bulletin of the Korean Chemical Society (BKCS)

ISSN 0253-2964(Print)
ISSN 1229-5949(Online)
Volume 30, Number 11
BKCSDE 30(11)
November 20, 2009 

 
Title
Classification and Regression Tree Analysis for Molecular Descriptor Selection and Binding Affinities Prediction of Imidazobenzodiazepines in Quantitative Structure-Activity Relationship Studies
Author
Morteza Atabati, Kobra Zarei*, Esmaeil Abdinasab
Keywords
CART, QSAR, Imidazobenzodiazepines, Binding affinity, Benzodiazepine receptor
Abstract
The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-activity relationship (QSAR) context on a data set consisting of the binding affinities of 39 imidazobenzodiazepines for the α1 benzodiazepine receptor. The 3-D structures of these compounds were optimized using HyperChem software with semiempirical AM1 optimization method. After optimization a set of 1481 zero-to three-dimentional descriptors was calculated for each molecule in the data set. The response (dependent variable) in the tree model consisted of the binding affinities of drugs. Three descriptors (two topological and one 3D-Morse descriptors) were applied in the final tree structure to describe the binding affinities. The mean relative error percent for the data set is 3.20%, compared with a previous model with mean relative error percent of 6.63%. To evaluate the predictive power of CART cross validation method was also performed.
Page
2717 - 2722
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