期刊
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
卷 43, 期 1, 页码 113-119出版社
AMER CHEMICAL SOC
DOI: 10.1021/ci020013r
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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.
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