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Quantitative Structure Activity Relationship Prediction of Oral Bioavailabilities Using Support Vector Machine
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 Title & Authors
Quantitative Structure Activity Relationship Prediction of Oral Bioavailabilities Using Support Vector Machine
Fatemi, Mohammad Hossein; Fadaei, Fatemeh;
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A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}
Quantitative structure activity relationship;Support vector machine;Multiple linear regressions;Oral bioavailability;Molecular descriptors;
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