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Int. J. Mol. Sci. 2009, 10(8), 3316-3337; doi:10.3390/ijms10083316

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

Institute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China
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Received: 30 June 2009 / Revised: 20 July 2009 / Accepted: 22 July 2009 / Published: 29 July 2009
(This article belongs to the Section Physical Chemistry, Theoretical and Computational Chemistry)
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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. View Full-Text
Keywords: classification; 5-HT1A selective ligands; topological descriptor; particle swarm optimization; Adaboost-SVM classification; 5-HT1A selective ligands; topological descriptor; particle swarm optimization; Adaboost-SVM
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MDPI and ACS Style

Cheng, Z.; Zhang, Y.; Zhou, C.; Zhang, W.; Gao, S. Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM. Int. J. Mol. Sci. 2009, 10, 3316-3337.

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