Classification of 5-HT(1A) receptor ligands on the basis of their binding affinities by using PSO-Adaboost-SVM.
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-HT(1A) 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.
Cheng
Zhengjun
Z
Institute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.
Zhang
Yuntao
Y
Institute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.
Zhou
Changhong
C
Institute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.
Zhang
Wenjun
W
Institute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.
Gao
Shibo
S
Institute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.
D000465:Algorithms; D007700:Kinetics; D008024:Ligands; D008958:Models, Molecular; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D044282:Receptor, Serotonin, 5-HT1A / Q000737:chemistry* / Q000378:metabolism; D060388:Support Vector Machine
D008024:Ligands; D044282:Receptor, Serotonin, 5-HT1A