Chemistry of Synthetic Musks I. Nonbenzenoid Musks

Wood, Tabitha E.

doi:10.1016/b978-0-12-685850-1.50018-8

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…All compounds used in this study were taken from literature reports of chemical structure and odor quality. − A list of the compounds comprising the database is given in Table . The 192 macrocyclic and nitroaromatic musks are of strong, medium, weak, or of unspecified odor intensity, whereas the 139 nonmusks are odorless or have an odor other than musk.…”

Section: Data Setmentioning

confidence: 99%

Electronic van der Waals Surface Property Descriptors and Genetic Algorithms for Developing Structure−Activity Correlations in Olfactory Databases

Lavine¹,

Davidson²,

Breneman³

et al. 2003

J. Chem. Inf. Comput. Sci.

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A methodology to facilitate the intelligent design of new odorants (e.g., musks) with specialized properties has been developed as part of an ongoing research effort in machine learning. In a traditional framework, the introduction of a new odorant is a lengthy, costly, and laborious discovery, development, and testing process. We propose to streamline this process utilizing large existing olfactory databases available through the open scientific literature as input for a new structure/activity correlation methodology. The first step in this process is to characterize each molecule in the database by an appropriate set of descriptors. To accomplish this task, an enhanced version of Breneman's Transferable Atom Equivalent (TAE) descriptor methodology will be used to create a large set of electron density derived shape/property hybrid (PEST), wavelet coefficient (WCD), and TAE histogram descriptors. We have chosen these molecular property descriptors to represent the problem because they have been shown to contain pertinent shape and electronic properties of the molecule and correlate with key modes of intermolecular interactions. Traditional QSAR methodologies, which employ fragment based descriptors, have been shown to be effective for QSAR development within homologous sets of molecules but are less effective when applied to data sets containing a great deal of structural variation. In contrast to previous attempts at SAR, our use of shape-aware electron density based molecular property descriptors has removed many of the limitations brought about by the use of descriptors based on substructure fragments, molecular surface properties, or other whole molecule descriptors. Another reason for the mixed success of past QSAR efforts can be traced to the nature of the underlying modeling problem, which is often quite complex. To meet these challenges, a genetic algorithm for pattern recognition analysis has been developed that selects descriptors which create class separation in a plot of the two largest principal components of the data while simultaneously searching for features that increase clustering of the data.

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Section: Data Setmentioning

confidence: 99%

Electronic van der Waals Surface Property Descriptors and Genetic Algorithms for Developing Structure−Activity Correlations in Olfactory Databases

Lavine¹,

Davidson²,

Breneman³

et al. 2003

J. Chem. Inf. Comput. Sci.

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“…An olfactory database of 187 compounds was compiled from the published literature (28)(29)(30)(31)(32)(33)(34)(35) for investigating the relationship between chemical structure and musk odor quality. For this study, nonmusks were chosen to be as similar in structure to the musks as possible (see Figure 1).…”

Section: Experimental Datasetmentioning

confidence: 99%

Odor-Structure Relationship Studies of Indan, Tetralin, and Isochroman Musks

Lavine

White

2015

ACS Symposium Series

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One hundred and eighty-seven tetralin-, indan-, and isochroman-like compounds were combed from the published literature to investigate the relationship between chemical structure and musk odor quality.Utilizing Breneman's transferable atom equivalent (TAE) and property encoded surface translator (PEST) descriptors and comprehensive descriptors for structural and statistical analysis (CODESSA), each compound in this database was represented by 1369 descriptors. A genetic algorithm (GA) for pattern recognition analysis was used to identify a subset of the 1369 molecular descriptors that could differentiate musks from nonmusks in a plot of the two largest principal components of the data. The 20 molecular structural descriptors selected by the pattern recognition GA for variable selection contained information about the shape, electronic properties, and intermolecular interactions of these compounds. Due to the risk of model interpretation when performing variable selection, model cross-validation was performed using an external validation set of 19 compounds. Discriminants (in the form of a principal component plot of the 20 molecular descriptors and the 168 compounds comprising the training set) were successfully validated using the external validation set of 19 compounds.

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“…During the last few years, the search for new musk compounds has shifted towards the non-nitromusks since, from about 1980 onwards, the use of nitromt~sks has been put into question because it was suspected that they might have phototoxic and photoallergic properties and might possibly even cause liver cancer. The first compound described in this paper, musk phantolid, was discovered in the early 1950's by Kurt Fuchs (Wood, 1968). Like musk tonalid, its success can be attributed to its excellent odour and fixative properties in which it simulates the macrocyclic musks in their outstanding stability toward alkali and light.…”

Section: Laboratory Formentioning

confidence: 99%