Chastrette M, De Saint Laumer J Y, Peyraud J F
Laboratoire de Chimie Organique Physique, URA CNRS No. 463, Université Claude Bernard-Lyon I, France.
SAR QSAR Environ Res. 1993;1(2-3):221-31. doi: 10.1080/10629369308028830.
Two types of neural networks were used to establish relationships between chemical structure and musk odour of 79 nitrobenzenic compounds. Substituents on the five free sites of the benzene ring (one position was always occupied by a t-butyl group) were described using three volume descriptors and three electronegativity descriptors. Musk odour was coded by a binary variable. First a classical network with two hidden layers containing six and three neurons was used. This network gave a better classification (94%) than that obtained by linear discriminant analysis (81%). The odour was then predicted using a leave-ten-out procedure, with 77% of correct prediction for the whole sample. Then a dual two-way network was built to mimic the symmetry of the problem (two sides on a molecule, two muskophore patterns). This network recognized both patterns already known to chemists and gave 99% of correct classifications by taking into account substitution in all positions. As a side benefit of the modified network structure it was possible to evaluate the influence of each of 19 substituents in each of the five possible positions.
使用两种神经网络来建立79种硝基苯类化合物的化学结构与麝香气味之间的关系。苯环上五个自由位点（其中一个位置始终被叔丁基占据）上的取代基使用三个体积描述符和三个电负性描述符进行描述。麝香气味由一个二元变量编码。首先使用一个具有两个隐藏层、分别包含六个和三个神经元的经典网络。该网络给出了比线性判别分析（81%）更好的分类结果（94%）。然后使用留十法进行气味预测，整个样本的正确预测率为77%。接着构建了一个对偶双向网络来模拟问题的对称性（分子的两侧，两种麝香团模式）。该网络识别出了化学家们已经知道的两种模式，并且通过考虑所有位置的取代情况给出了99%的正确分类。作为修改后的网络结构的一个附带好处，可以评估19种取代基中每一种在五个可能位置中的每一个位置上的影响。
