A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine.
In this work some chemometrics methods were applied for modeling and prediction of the induction of apoptosis by 4-aryl-4-H-chromenes with descriptors calculated from the molecular structure alone. The genetic algorithm (GA) and stepwise multiple linear regression methods were used to select descriptors which are responsible for the apoptosis-inducing activity of these compounds. Then support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR) were utilized to construct the nonlinear and linear quantitative structure-activity relationship models. The obtained results using SVM were compared with ANN and MLR; it revealed that the GA-SVM model was much better than other models. The root-mean-square errors of the training set and the test set for GA-SVM model are 0.181, 0.241 and the correlation coefficients were 0.950, 0.924, respectively, and the obtained statistical parameters of cross validation test on GA-SVM model were Q(2)=0.71 and SRESS=0.345 which revealed the reliability of this model. The results were also compared with previous published model and indicate the superiority of the present GA-SVM model.
Fatemi
Mohammad Hossein
MH
Department of Chemistry, Mazandaran University, Babolsar, Iran.
Gharaghani
Sajjad
S
D000465:Algorithms; D017209:Apoptosis / Q000187:drug effects; D001578:Benzopyrans / Q000737:chemistry* / Q000494:pharmacology; D008954:Models, Biological; D008958:Models, Molecular; D015394:Molecular Structure; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000706:statistics & numerical data; D021281:Quantitative Structure-Activity Relationship
D001578:Benzopyrans