STRUCTURE ODOR RELATIONSHIPS USING NEURAL NETWORKS

被引:19
作者
CHASTRETTE, M
LAUMER, JYD
机构
[1] Laboratoire de Chimie Organique Physique, Université Lyon I, 69622 Villeurbanne Cedex, 43, bd du
关键词
STRUCTURE ACTIVITY RELATIONSHIP; MUSK ODOR; NEURAL NETWORK; DISCRIMINANT ANALYSIS;
D O I
10.1016/0223-5234(91)90010-K
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Models of relationships between structure and musk odor of 79 nitrobenzenic compounds were constructed by means of a multilayer neural network using the backpropagation algorithm. Substituents on the 5 free sites on the benzenic ring (one position is always substituted by a terbutyl group) were described using 3 steric hindrance descriptors and 3 electronegativity descriptors. Odor was coded by a binary variable. Our neural network gives a better classification (94%) than that obtained by discriminant analysis (81 %). The activity of the 79 molecules was then predicted (77% correct prediction) for 8 subsets of 10 (or 9) compounds using 69 (or 70) other molecules as a neural network training set.
引用
收藏
页码:829 / 833
页数:5
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