Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem

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).

Odorant Receptor Inhibition Is Fundamental to Odor Encoding

“…High-throughput screening of $800 volatile compounds against the 10 ORs identified through single-cell transcriptomics and phylogenetics allowed us to probe the prevalence of both agonism and antagonism for a much larger chemical space. While this library is small compared with those typically found in drug screens (where compounds can number in the millions), it is large compared with what has previously been shown in the olfactory literature and is approximately 25% as large as all the odorants reportedly described in public databases ($3,100) [53]. Over half of the library was found to antagonize one or more ORs with about one third of those showing specificity to single ORs and the rest antagonizing multiple.…”

Odorant Receptor Inhibition is Fundamental to Odor Encoding

“…The high-throughput screening of ~800 volatile compounds against the Olfr743 family allowed us to probe the prevalence of both agonism and antagonism for a much larger chemical space than the original diagnostic ligands. While this library is small compared to those typically found in drug screens (in which compounds can number in the millions), it is large compared to what has previously been reported in the olfactory literature and is approximately 25% as large as all the odorants reportedly described in public databases (~3100) [8]. We identified a total of 430 putative antagonists across the receptor family, of which 149 were specific to a single receptor, and the rest shared across multiple members.…”

“…Using these data, we constructed receiver operating characteristic (ROC) curves for each screen. The area under the curve (AUC) for the antagonist screen was 0.78, p < 0.0001; 8 AUC for the agonist screen was 0.90, p < 0.0001; ( Figure S7). From this analysis, 75% of true antagonists showed inhibition greater than 24% in single-concentration antagonist screens and 80% of true agonists showed activation greater than 32% in single-concentration agonist screens.…”

“…First, estimates of the actual number of odors humans can distinguish are controversial, and have so far resisted reliable empirical determination [1][2][3][4][5]. Compounding this, the chemical diversity of odorant space is extensive and difficult to quantify [6][7][8][9], and the complexity of natural odors is also typically high. The same source can exhibit a range of compositions, with component concentrations varying considerably [10,11].…”

Odors: from chemical structures to gaseous plumes

“…what it smells like) (Olofsson et al, 2012(Olofsson et al, , 2013. Additionally, Kumar et al (2015) created an alternative computational model to that of Snitz et al (2013) using descriptors of odor qualities and not judgements of valence, as well as measures of chemical structures to predict olfactory quality. Similarly, Menzel et al's (2019) test for human olfactory change detection only yielded reliable detection for 24% of the participants, yet across all individuals olfactory quality was detected with greater frequency than concentration, which we take to further solidify the primacy of odor quality for the purposes of odor identity.…”