QSAR study on melanocortin-4 receptors by support vector machine.
作者： Eslam Pourbasheer , Siavash Riahi , Mohammad Reza Ganjali , Parviz Norouzi
DOI: 10.1016/J.EJMECH.2009.12.003
关键词: Support vector machine 、 Stereochemistry 、 Affinities 、 Linear regression 、 Quantitative structure–activity relationship 、 Pattern recognition 、 Chemometrics 、 Regression analysis 、 Chemistry 、 Test set 、 Artificial intelligence 、 Molecular descriptor
摘要: Abstract Quantitative structure activity relationship (QSAR) of the melanocortin-4 receptor (MC4R) binding affinities (Ki) trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides piperazinecyclohexanes was studied. A suitable set molecular descriptors calculated and genetic algorithm (GA) employed to select those that resulted in best-fit models. The multiple linear regression (MLR), support vector machine (SVM) were utilized construct nonlinear QSAR models validated using Leave-One-Out (LOO) Leave-Group-Out (LGO) cross-validation, external test set, chance correlation. SVM model generalizes better than MLR model. model, with high statistical significance (R2train = 0.908, Q2LOO = 0.781, Q2LGO = 0.872), could be used predict piperazinecyclohexanes.
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