International Journal of Molecular Sciences | |
Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM | |
Zhengjun Cheng2  Yuntao Zhang1  Changhong Zhou2  Wenjun Zhang2  | |
[1] Institute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China | |
关键词: classification; 5-HT1A selective ligands; topological descriptor; particle swarm optimization; Adaboost-SVM; | |
DOI : 10.3390/ijms10083316 | |
来源: mdpi | |
【 摘 要 】
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.
【 授权许可】
CC BY
© 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.
【 预 览 】
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