Support vector machine for SAR/QSAR of phenethyl-amines
Abstract
Aim: To discriminate 32 phenethyl-amines between antagonists and agonists, and predict the activities of these compounds.
Methods: The support vector machine (SVM) is employed to investigate the structure-activity relationship (SAR)/quantitative structure-activity relationship (QSAR) of phenethyl-amines based on molecular descriptors.
Results: By using the leave-one-out cross-validation (LOOCV) test, 1 optimal SAR and 2 optimal QSAR models for agonists and antagonists were attained. The accuracy of prediction for the classification of phenethyl-amines by using the LOOCV test is 91.67%, and the accuracy of prediction for teh classification of phenethyl-amines by using the independent test is 100%; the results are better than those of the Fisher, the artificial neural network (ANN), and the K-nearest neighbor models for this real world data. The RMSE (root mean square error) of antagonists' QSAR model is 0.5881, and the RMSE of agonists' QSAR model is 0.4779, which are better than those of the multiple linear regression, partial least squares, and ANN models for this real world data.
Conclusion: The SVM can be used to investigate the SAR and QSAR of phenethylamines and could be a promising tool in the field of SAR/QSAR research.
Keywords:
Methods: The support vector machine (SVM) is employed to investigate the structure-activity relationship (SAR)/quantitative structure-activity relationship (QSAR) of phenethyl-amines based on molecular descriptors.
Results: By using the leave-one-out cross-validation (LOOCV) test, 1 optimal SAR and 2 optimal QSAR models for agonists and antagonists were attained. The accuracy of prediction for the classification of phenethyl-amines by using the LOOCV test is 91.67%, and the accuracy of prediction for teh classification of phenethyl-amines by using the independent test is 100%; the results are better than those of the Fisher, the artificial neural network (ANN), and the K-nearest neighbor models for this real world data. The RMSE (root mean square error) of antagonists' QSAR model is 0.5881, and the RMSE of agonists' QSAR model is 0.4779, which are better than those of the multiple linear regression, partial least squares, and ANN models for this real world data.
Conclusion: The SVM can be used to investigate the SAR and QSAR of phenethylamines and could be a promising tool in the field of SAR/QSAR research.