文章基本信息

标题：Prediction of Human Skin Permeability Using a Combination of Molecular Orbital Calculations and Artificial Neural Network
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作者：Chee Wooi Lim ; Shin-ichi Fujiwara ; Fumiyoshi Yamashita 等
期刊名称：Biological and Pharmaceutical Bulletin
印刷版ISSN：0918-6158
电子版ISSN：1347-5215
出版年度：2002
卷号：25
期号：3
页码：361-366
DOI：10.1248/bpb.25.361
出版社：The Pharmaceutical Society of Japan
摘要：This study was carried out to develop a novel method for predicting the skin permeability coefficient (log K _p ) of compounds from their three-dimensional molecular structure using a combination of molecular orbital (MO) calculation and artificial neural network. Human skin permeability data on 92 structurally diverse compounds were analyzed. The molecular descriptors of each compound, such as the dipole moment, polarizability, sum of charges of nitrogen and oxygen atoms (sum(N, O)), and sum of charges of hydrogen atoms bonding to nitrogen or oxygen atoms (sum(H)) were obtained from MO calculations. The correlation between these molecular descriptors and log K _p was examined using feed-forward back-propagation neural networks. To improve the generalization capability of a neural network, the network was trained with input patterns given 5% random noise. The neural network model with a configuration of 4–4–1 for input, hidden, and output layers was much superior to the conventional multiple linear regression model in terms of root mean square (RMS) errors (0.528 vs. 0.930). A “leave-one-out” cross-validation revealed that the neural network model could predict skin permeability with a reasonable accuracy (predictive RMS error of 0.669).
关键词：skin permeability;molecular orbital calculation;artificial neural network;quantitative structure/permeability relationship