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  • Title: A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine.
    Author: Fatemi MH, Gharaghani S.
    Journal: Bioorg Med Chem; 2007 Dec 15; 15(24):7746-54. PubMed ID: 17870538.
    Abstract:
    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.
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