Wongrattanakamon Pathomwat, Lee Vannajan Sanghiran, Nimmanpipug Piyarat, Jiranusornkul Supat
Laboratory for Molecular Design and Simulation (LMDS), Department of Pharmaceutical Sciences, Faculty of Pharmacy, Chiang Mai University, Chiang Mai 50200, Thailand.
Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia.
Data Brief. 2016 Aug 4;9:35-42. doi: 10.1016/j.dib.2016.08.004. eCollection 2016 Dec.
The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion.
这些数据是通过基于配体的方法探索生物类黄酮对P-糖蛋白功能的调节活性而获得的。建立了用于预测黄酮类化合物诱导/抑制活性的多元线性定量构效关系（QSAR）模型。分子描述符最初用作自变量，因变量表示为pFAR。然后通过逐步回归计算将这些变量用于多元线性回归（MLR）分析，以建立线性QSAR数据。整个数据集由23种生物类黄酮组成，用作训练集。关于所获得的MLR QSAR模型，得到的R为0.963，R² = 0.9
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