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Predicting human plasma protein binding of drugs using plasma protein interaction QSAR analysis (PPI-QSAR).

机译:使用血浆蛋白相互作用QSAR分析(PPI-QSAR)预测药物与人血浆蛋白的结合。

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摘要

A novel method, named as the plasma protein-interaction QSAR analysis (PPI-QSAR) was used to construct the QSAR models for human plasma protein binding. The intra-molecular descriptors of drugs and inter-molecular interaction descriptors resulted from the docking simulation between drug molecules and human serum albumin were included as independent variables in this method. A structure-based in silico model for a data set of 65 antibiotic drugs was constructed by the multiple linear regression method and validated by the residual analysis, the normal Probability-Probability plot and Williams plot. The R(2) and Q(2) values of the entire data set were 0.87 and 0.77, respectively, for the training set were 0.86 and 0.72, respectively. The results indicated that the fitted model is robust, stable and satisfies all the prerequisites of the regression models. Combining intra-molecular descriptors with inter-molecular interaction descriptors between drug molecules and human serum albumin, the drug plasma protein binding could be modeled and predicted by the PPI-QSAR method successfully.
机译:一种新的方法被称为血浆蛋白相互作用QSAR分析(PPI-QSAR),用于构建人血浆蛋白结合的QSAR模型。该方法将药物的分子内描述符和药物分子与人血清白蛋白对接模拟产生的分子间相互作用描述符作为独立变量。通过多元线性回归方法构建了65种抗生素药物数据集的基于结构的计算机模型,并通过残差分析,正态概率-概率图和威廉姆斯图进行了验证。整个数据集的R(2)和Q(2)值分别为0.87和0.77,对于训练集,分别为0.86和0.72。结果表明,拟合模型是鲁棒的,稳定的,并且满足回归模型的所有先决条件。将分子内描述子与药物分子与人血清白蛋白之间的分子间相互作用描述子结合起来,可以成功地通过PPI-QSAR方法对药物血浆蛋白结合进行建模和预测。

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