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First-principle, structure-based prediction of hepatic metabolic clearance values in human.

机译:基于原理的基于结构的人体肝脏代谢清除率预测。

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摘要

The first-principle, quantitative structure-hepatic clearance relationship for 50 drugs was constructed based on selected molecular descriptors calculated by TSAR software. The R(2) of the predicted and observed hepatic clearance for the training set (n=36) and test set (n=13) were 0.85 and 0.73, respectively. The average fold error (AFE) of the in silico model was 1.28 (n=50). The prediction accuracy of in silico model was superior to in vitro hepatocytes' model in literature (n=50, AFE=2.55). It is attractive to predict human hepatic clearance based on molecular descriptors merely. The structure-based model can be used as an efficient tool in the rapid identification of hepatic clearance of new drug candidates in drug discovery.
机译:基于TSAR软件计算的选定分子描述符,建立了50种药物的第一性原理,定量结构与肝清除率的关系。训练组(​​n = 36)和测试组(n = 13)的肝清除率的预测值和观察值的R(2)分别为0.85和0.73。计算机模型的平均折叠误差(AFE)为1.28(n = 50)。在文献中,计算机模型的预测准确性优于体外肝细胞模型(n = 50,AFE = 2.55)。仅基于分子描述符预测人类肝清除率是有吸引力的。基于结构的模型可以用作快速发现新药候选药物的肝清除率的有效工具。

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