Abstract:
:A list of 147 tetralin- and indan-like compounds was compiled from the literature for investigating the relationship between molecular structure and musk odor. Each compound in the data set was represented by 374 CODESSA and 970 TAE descriptors. A genetic algorithm (GA) for pattern recognition analysis was used to identify a subset of molecular descriptors that could differentiate musks from nonmusks in a plot of the two largest principal components (PCs) of the data. A PC map of the 110 compounds in the training set using 45 molecular descriptors identified by the pattern recognition GA revealed an asymmetric data structure. Tetralin and indan musks were found to occupy a small, but well-defined region of the PC (descriptor) space, with the nonmusks randomly distributed in the PC plot. A three-layer feed-forward neural network trained by back propagation was used to develop a discriminant that correctly classified all the compounds in the training set as musk or nonmusk. The neural network was successfully validated using an external prediction of 37 compounds.
journal_name
Chem Sensesjournal_title
Chemical sensesauthors
Lavine BK,White C,Mirjankar N,Sundling CM,Breneman CMdoi
10.1093/chemse/bjs058subject
Has Abstractpub_date
2012-10-01 00:00:00pages
723-36issue
8eissn
0379-864Xissn
1464-3553pii
bjs058journal_volume
37pub_type
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