Predicting Odor Perceptual Similarity from Odor Structure

Abstract

To understand the brain mechanisms of olfaction we must understand the rules that govern the link between odorant structure and odorant perception. Natural odors are in fact mixtures made of many molecules, and there is currently no method to look at the molecular structure of such odorant-mixtures and predict their smell. In three separate experiments, we asked 139 subjects to rate the pairwise perceptual similarity of 64 odorant-mixtures ranging in size from 4 to 43 mono-molecular components. We then tested alternative models to link odorant-mixture structure to odorant-mixture perceptual similarity. Whereas a model that considered each mono-molecular component of a mixture separately provided a poor prediction of mixture similarity, a model that represented the mixture as a single structural vector provided consistent correlations between predicted and actual perceptual similarity (r≥0.49, p<0.001). An optimized version of this model yielded a correlation of r = 0.85 (p<0.001) between predicted and actual mixture similarity. In other words, we developed an algorithm that can look at the molecular structure of two novel odorant-mixtures, and predict their ensuing perceptual similarity. That this goal was attained using a model that considers the mixtures as a single vector is consistent with a synthetic rather than analytical brain processing mechanism in olfaction.

Configural and Elemental Coding of Natural Odor Mixture Components in the Human Brain

“…The majority of realworld smells are composed of complex mixtures of dozens, if not hundreds, of unique molecular components, each of which can in turn be described by myriad physicochemical parameters. Perhaps not surprisingly then, central processing of multidimensional odor stimuli is highly configural, such that complex odor mixtures are perceived as unified wholes, with no conscious access to physical stimulus features (Kay et al, 2005; Laing and Francis, 1989; Snitz et al, 2013). …”

Quantum Calculation for Musk Molecules Infrared Spectra towards the Understanding of Odor

“…There are major recent advances on neurobiology, biophysics, biochemical fields [4][5][6][7], though it is still unclear how humans recognize odor. At this scenario, quantitative structure-activity relationship models (QSAR models) are valuable for the proposal of new potential musky smelling molecules [8][9][10][11][12]. Such approach allows for the reduction in the number of costly and unnecessary chemical syntheses.…”

“…A mathematical formula had to be developed (7), indane (8), tetralin (9), and tonkene (10); nitromusks (NM): musk ketone (11), musk moskene (12), and musk ambrette (13); acyclic musks (ACM): romandolide (14), helvetolide (15), and cyclomusk (16); nonmusks: macrocyclic analogs (MCa) (17) and (18), acyclic analogs (Aa) (19) and (20), and nitro analogs (Na) (21), (22), and (23).…”

“…When repeating this process for all three bands under the spectra, values for the Total Deviations (TD) are collected. The zero input (0) means that all three bands obeyed (8). Logically, TD inputs between 1 and 3 indicate how many of the bands presented a diff value that was higher than the std group doubled value.…”

Olfactory receptors are sensitive to molecular volume of odorants

“…The combinatorial code helps the olfactory system to discriminates trillion odors [12]. However, it is not clear yet which properties of a molecule contribute to its smell, it is a topic of ongoing researches and there are many theories [13][14][15][16][17][18][19][20][21][22][23][24].…”