Abstract
Odours are highly complex, relying on hundreds of receptors, and people are known to disagree in their linguistic descriptions of smells. It is partly due to these facts that, it is very hard to map the domain of odour molecules or their structure to that of perceptual representations, a problem that has been referred to as the Structure-Odour-Relationship. We collected a number of diverse open domain databases of odour molecules having unorganised perceptual descriptors, and developed a graphical method to find the similarity between perceptual descriptors; which is intuitive and can be used to identify perceptual classes. We then separately projected the physico-chemical and perceptual features of these molecules in a non-linear dimension and clustered the similar molecules. We found a significant overlap between the spatial positioning of the clustered molecules in the physico-chemical and perceptual spaces. We also developed a statistical method of predicting the perceptual qualities of a novel molecule using its physico-chemical properties with high receiver operating characteristics(ROC).
Highlights
There are not many answers to the question of why a molecule smells as it smells
This trend is different in Leon and Johnson database (LJ) where most of the molecules have more than three perceptual descriptors
We have systematically compared the odour network with corresponding semantic networks and analysed, if the positioning of the perceptual descriptors is only due to their semantic relatedness
Summary
There are not many answers to the question of why a molecule smells as it smells. Researchers have been working to find models that can predict how a molecule smells based on its physicochemical properties [1,2]. The first hurdle itself has been hard to fathom i.e. how do you objectively define perceptual descriptors? "musk") were undertaken [4,5,6]. Efforts towards defining specific molecular properties which could account for a very specific perceptual descriptor These failed to define a general rule to predict the perceptual descriptor of a molecule.
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