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Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM

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Author(s): Zhengjun Cheng | Yuntao Zhang | Changhong Zhou | Wenjun Zhang | Shibo Gao

Journal: International Journal of Molecular Sciences
ISSN 1422-0067

Volume: 10;
Issue: 8;
Start page: 3316;
Date: 2009;
Original page

Keywords: classification | 5-HT1A selective ligands | topological descriptor | particle swarm optimization | Adaboost-SVM

ABSTRACT
In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies.
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Tango Rapperswil
Tango Rapperswil