Dastmalchi Siavoush, Hamzeh-Mivehroud Maryam, Asadpour-Zeynali Karim
Department of Medicinal Chemistry , School of Pharmacy , Tabriz University of Medical Sciences , Tabriz , Iran . ; Biotechnology Research Center , Tabriz University of Medical Sciences , Tabriz , Iran .
Department of Analytical Chemistry , Faculty of Chemistry , University of Tabriz , Tabriz , Iran .
Iran J Pharm Res. 2012 Winter;11(1):97-108.
Histamine H3 receptor subtype has been the target of several recent drug development programs. Quantitative structure-activity relationship (QSAR) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. The aim of this study was to compare the predictive powers of three different QSAR techniques, namely, multiple linear regression (MLR), artificial neural network (ANN), and HASL as a 3D QSAR method, in predicting the receptor binding affinities of arylbenzofuran histamine H3 receptor antagonists. Genetic algorithm coupled partial least square as well as stepwise multiple regression methods were used to select a number of calculated molecular descriptors to be used in MLR and ANN-based QSAR studies. Using the leave-group-out cross-validation technique, the performances of the MLR and ANN methods were evaluated. The calculated values for the mean absolute percentage error (MAPE), ranging from 2.9 to 3.6, and standard deviation of error of prediction (SDEP), ranging from 0.31 to 0.36, for both MLR and ANN methods were statistically comparable, indicating that both methods perform equally well in predicting the binding affinities of the studied compounds toward the H3 receptors. On the other hand, the results from 3D-QSAR studies using HASL method were not as good as those obtained by 2D methods. It can be concluded that simple traditional approaches such as MLR method can be as reliable as those of more advanced and sophisticated methods like ANN and 3D-QSAR analyses.
组胺H3受体亚型已成为近期多个药物研发项目的靶点。只要适用，定量构效关系（QSAR）方法就被用于预测候选药物的药学相关性质。本研究的目的是比较三种不同QSAR技术，即多元线性回归（MLR）、人工神经网络（ANN）以及作为三维QSAR方法的HASL，在预测芳基苯并呋喃组胺H3受体拮抗剂的受体结合亲和力方面的预测能力。采用遗传算法耦合偏最小二乘法以及逐步多元回归方法，选择一些计算得到的分子描述符用于基于MLR和ANN的QSAR研究。使用留组交叉验证技术评估MLR和ANN方法的性能。MLR和ANN方法的平均绝对百分比误差（MAPE）计算值在2.9至3.6之间，预测误差标准差（SDEP）在0.31至0.36之间，在统计学上具有可比性，这表明两种方法在预测所研究化合物与H3受体的结合亲和力方面表现同样出色。另一方面，使用HASL方法进行的三维QSAR研究结果不如二维方法得到的结果好。可以得出结论，简单的传统方法如MLR方法与更先进复杂的方法如ANN和三维QSAR分析一样可靠。
