Using general regression and probabilistic neural networks to predict human intestinal absorption with topological descriptors derived from two-dimensional chemical structures
Using general regression and probabilistic neural networks to predict human intestinal absorption with topological descriptors derived from two-dimensional chemical structures
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DOI：
10.1021/ci020013r
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发表时间：
2003-01-01
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影响因子：
--
通讯作者：
Niwa, T
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文献类型：
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作者：
Niwa, T
本研究的目的是开发快速可靠的方法来预测基于其二维描述符的化合物的人体肠道吸收百分比（%HIA）。分析的数据集包括86种药物和药物样分子，与Wessel及其同事研究的数据集相同。我们没有使用三维描述符，如极性表面积，这需要冗长的计算，而是仅使用来自分子二维结构信息的二维拓扑描述符。使用通用回归神经网络（GRNN）和概率神经网络（PNN）（归一化径向基函数网络的变体）对%HIA值进行建模。两个网络都表现良好，可以对%HIA值进行建模。GRNN模型外部预测集的均方根（rms）误差为22.8% HIA单位。80%的外部预测集被正确分类为PNN模型，表明我们的方法估计作为虚拟库的大量化合物的%HIA值的潜力。
The objective of this study was to develop rapid and reliable methods to predict the percent human intestinal absorption (%HIA) of compounds based on their 2D descriptors. The analyzed data set included 86 drug and drug-like molecules and was the same as that studied by Wessel and co-workers, Instead of using three-dimensional descriptors such as polar surface area, which require lengthy computations, we employed only two-dimensional topological descriptors derived from information about the two-dimensional structure of molecules. The %HIA Values were modeled using, general regression neural network (GRNN) and a probabilistic neural network (PNN), variants of normalized radial basis function networks. Both networks performed well to model the %HIA values. The root-mean square (rms) error was 22.8 %HIA unit for the external prediction set for a GRNN model. and 80% of the external prediction set was correctly classified for a PNN model, indicating the potential of our approach to estimate the %HIA values for a large set of compounds as virtual libraries.