Faculty of Chemistry, University of Tehran, Center of Excellence in Electrochemistry, Tehran, Iran.
Eur J Med Chem. 2010 Mar;45(3):1087-93. doi: 10.1016/j.ejmech.2009.12.003. Epub 2009 Dec 23.
Quantitative structure activity relationship (QSAR) of the melanocortin-4 receptor (MC4R) binding affinities (K(i)) of trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides of piperazinecyclohexanes was studied. A suitable set of molecular descriptors was calculated and the genetic algorithm (GA) was employed to select those descriptors that resulted in the best-fit models. The multiple linear regression (MLR), and the support vector machine (SVM) were utilized to construct the linear and nonlinear QSAR models. The models were validated using Leave-One-Out (LOO) and Leave-Group-Out (LGO) cross-validation, external test set, and chance correlation. The SVM model generalizes better than the MLR model. The SVM model, with high statistical significance (R(2)(train)=0.908, Q(2)(LOO)=0.781, Q(2)(LGO)=0.872), could be used to predict melanocortin-4 receptor binding affinities of piperazinecyclohexanes.
研究了黑皮质素-4 受体（MC4R）结合亲和力（Ki）的定量构效关系（QSAR），反式-4-（4-氯苯基）吡咯烷-3-甲酰胺的哌嗪环己烷。计算了一组合适的分子描述符，并采用遗传算法（GA）选择那些导致最佳拟合模型的描述符。多元线性回归（MLR）和支持向量机（SVM）用于构建线性和非线性 QSAR 模型。使用留一法（LOO）和留组法（LGO）交叉验证、外部测试集和机会相关性对模型进行验证。SVM 模型比 MLR 模型具有更好的泛化能力。SVM 模型具有很高的统计学意义（训练 R（2）（train）=0.908，LOO Q（2）（LOO）=0.781，LGO Q（2）（LGO）=0.872），可用于预测哌嗪环己烷的黑皮质素-4 受体结合亲和力。
