Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM
AbstractIn 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
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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.
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. International Journal of Molecular Sciences. 2009; 10(8):3316-3337.Chicago/Turabian Style
Cheng, Zhengjun; Zhang, Yuntao; Zhou, Changhong; Zhang, Wenjun; Gao, Shibo. 2009. "Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM." Int. J. Mol. Sci. 10, no. 8: 3316-3337.