It is still not possible to predict whether a given molecule will have a perceived odor, or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical dataset, teams developed machine learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness, and also successfully predicted eight among 19 rated semantic descriptors (“garlic”, “fish”, “sweet”, “fruit,” “burnt”, “spices”, “flower”, “sour”). Regularized linear models performed nearly as well as random-forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule.

Abstract In this study, we examined the mode of metabolism of food odorant molecules in the human nasal/oral cavity in vitro and in vivo. We selected 4 odorants, 2-furfurylthiol (2-FT), hexanal, benzyl acetate, and methyl raspberry ketone, which are potentially important for designing food flavors. In vitro metabolic assays of odorants with saliva/nasal mucus analyzed by gas chromatography mass spectrometry revealed that human saliva and nasal mucus exhibit the following 3 enzymatic activities: (i) methylation of 2-FT into furfuryl methylsulfide (FMS); (ii) reduction of hexanal into hexanol; and (iii) hydrolysis of benzyl acetate into benzyl alcohol. However, (iv) demethylation of methyl raspberry ketone was not observed. Real-time in vivo analysis using proton transfer reaction-mass spectrometry demonstrated that the application of 2-FT and hexanal through 3 different pathways via the nostril or through the mouth generated the metabolites FMS and hexanol within a few seconds. The concentration of FMS and hexanol in the exhaled air was above the perception threshold. A cross-adaptation study based on the activation pattern of human odorant receptors suggested that this metabolism affects odor perception. These results suggest that some odorants in food are metabolized in the human nasal mucus/saliva, and the resulting metabolites are perceived as part of the odor quality of the substrates. Our results help improve the understanding of the mechanism of food odor perception and may enable improved design and development of foods in relation to odor.

Background: It remains unclear whether the metabolic activity of nasal mucus in the olfactory and respiratory areas is different. Moreover, age-and olfactionrelated changes may affect metabolism.Methods: Hexanal, octanal, and 2-methylbutanal were selected for in vitro metabolism analysis and compared between the olfactory cleft and respiratory mucus of participants < 50-year-old with normal olfaction using gas chromatography mass spectrometry. The metabolic activity of hexanal in the olfactory cleft mucus was further compared between three groups, (1) normal olfaction, age < 50 years old, (2) normal olfaction, age ≥50 years old, and (3) idiopathic olfactory impairment. To characterize the enzyme(s) responsible for aldehyde reduction, we also tested if epalr22897estat and 3,5-dichlorosalicylic acid, types of reductase inhibitors, affect metabolism.Results: Conversion of aldehydes to their corresponding alcohols was observed in the olfactory cleft and respiratory mucus. The metabolic production of hexanol, octanol, and 2-methybutanol was significantly higher in the olfactory cleft mucus than in the respiratory mucus (p < 0.01). The metabolic conversion of hexanal to hexanol in the mucus of the idiopathic olfactory impairment group was significantly lower than that in the age-matched normal olfaction group. Excluding the nicotinamide adenine dinucleotide phosphate (NADPH) regenerating system from the reaction mixture inhibited metabolism. The addition of either epalr22897estat or 3,5-dichlorosalicylic acid did not inhibit this metabolic conversion. Conclusions:The enzymatic metabolism of odorants in the olfactory cleft mucus is markedly higher than in the respiratory mucus and decreases in patients with idiopathic olfactory impairment.

Despite 25 years of progress in understanding the molecular mechanisms of olfaction, it is still not possible to predict whether a given molecule will have a perceived odor, or what olfactory percept it will produce. To address this stimulus-percept problem for olfaction, we organized the crowd-sourced DREAM Olfaction Prediction Challenge. Working from a large olfactory psychophysical dataset, teams developed machine learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models predicted odor intensity and pleasantness with high accuracy, and also successfully predicted eight semantic descriptors ("garlic", "fish", "sweet", "fruit," "burnt", "spices", "flower", "sour").Regularized linear models performed nearly as well as random-forest-based approaches, with a predictive accuracy that closely approaches a key theoretical limit. The models presented here make it possible to predict the perceptual qualities of virtually any molecule with an impressive degree of accuracy to reverse-engineer the smell of a molecule. One Sentence Summary:Results of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule. Main Text:In vision and hearing, the wavelength of light and frequency of sound are highly predictive of color and tone. In contrast, it is not currently possible to predict the smell of a molecule from its chemical structure (1, 2). This stimulus-percept problem has been difficult to solve in olfaction because odor stimuli do not vary continuously in stimulus space, and the size and dimensionality of olfactory perceptual space is unknown (1, 3,4). Some molecules with very similar chemical structures can be discriminated by humans (5, 6), and molecules with very different structures sometimes produce nearly identical percepts (2). Recent computational efforts developed models to relate chemical structure to odor percept (2,(7)(8)(9)(10)(11), but many relied on psychophysical data from a single 30-year-old study that used odorants with limited structural and perceptual diversity (12, 13).Twenty-two teams competing in the DREAM Olfaction Prediction Challenge (14) were given a large, unpublished psychophysical dataset collected by Keller and Vosshall from 49 individuals who profiled 476 structurally and perceptually diverse molecules, including those that are unfamiliar, unpleasant, or nearly odorless (15) (Fig. 1a). We supplied 4884 physicochemical features of each of the molecules smelled by the subjects, including atom types, . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/082495 doi: bioRxiv preprint first posted online Oct. 21, 2016; 3 functional groups, and topological and geometrical properties that were computed using Dragon chemoinformatic software (version 6) (Fig. 1b).Using a baseline linear model developed for the challenge and inspi...