An olfaction database of over 300 compounds was compiled from the literature for the purpose of investigating the relationship between molecular structure and odor quality for polycyclic aromatic musks.  Each compound in the database was represented by CODESSA and TAE descriptors.  A genetic algorithm for pattern recognition analysis and variable selection was used to identify a subset of molecular descriptors that could differentiate musks from nonmusks in a plot of the two largest principal components of the data.  Three separate studies were undertaken, with 100% correct classification obtained in each case.  In the first study, 160 tetralin, indan and isochroman-like compounds were correctly classified as musk or nonmusk. The second study distinguished 61 bicyclo and tricyclo benzenoid musks from 81 nonmusks.  In the third study, a discriminant was developed that could differentiate polycyclic aromatic musks from nonmusks.  Discriminants developed in each of these studies were successfully validated using an external prediction set.  Molecular descriptors retained in the optimized discriminants convey information about molecular shape and the electronic surface properties of the compounds.  The results from these three studies when compared to a previous study published by our research group on macrocyclic and aromatic nitro musks indicate that nitro free polycyclic aromatic musks are characterized by a unique SAR.

Structure-activity relationship (SAR) studies of nitro free polycyclic aromatic musks

247th National Meeting (2014)

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