1. Chemical features mining provides new descriptive structure-odor relationships
- Author
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Moustafa Bensafi, Marc Plantevit, Caroline Bushdid, Mohammed Sabri, Mehdi Kaytoue, Arnaud Fournel, Céline Robardet, Guillaume Bosc, Jérôme Golebiowski, Marylou Mantel, Carmen C. Licon, Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Data Mining and Machine Learning (DM2L), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Institut de Chimie de Nice (ICN), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Neurosciences Sensorielles Comportement Cognition, Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon
- Subjects
Models, Molecular ,0301 basic medicine ,Science and Technology Workforce ,Chemical Phenomena ,Social Sciences ,Careers in Research ,computer.software_genre ,Physical Chemistry ,Rendering (computer graphics) ,[SCCO]Cognitive science ,Mathematical and Statistical Techniques ,0302 clinical medicine ,Psychology ,Data Mining ,Biology (General) ,Materials ,Sophistication ,ComputingMilieux_MISCELLANEOUS ,media_common ,Ecology ,Physics ,Statistics ,Smell ,Chemistry ,Professions ,Physicochemical Properties ,Computational Theory and Mathematics ,Modeling and Simulation ,Physical Sciences ,Sensory Perception ,Information Technology ,Algorithms ,Research Article ,Computer and Information Sciences ,QH301-705.5 ,Science Policy ,media_common.quotation_subject ,Materials Science ,Olfaction ,Research and Analysis Methods ,Machine learning ,Structure-Activity Relationship ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Phenols ,Perception ,Genetics ,Humans ,Statistical Methods ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Odor perception ,business.industry ,Cognitive Psychology ,Chemical Compounds ,Biology and Life Sciences ,Computational Biology ,Olfactory Perception ,Physical Properties ,030104 developmental biology ,Chemical Properties ,Odor ,Odorants ,People and Places ,Cognitive Science ,Scientists ,Population Groupings ,Artificial intelligence ,Percept ,business ,computer ,Mathematics ,Databases, Chemical ,030217 neurology & neurosurgery ,Neuroscience ,Forecasting - Abstract
An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants. In other words, why do some odorants smell like fruits and others like flowers? While the so-called stimulus-percept issue was resolved in the field of color vision some time ago, the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day. Although a series of investigations have demonstrated that this relationship exists, the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication. One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality, whereby two very different molecules can evoke a similar odor. Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction. In addition, by automatically extracting new knowledge linking chemistry of odorants and psychology of smells, our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling., Author summary An important issue in olfaction sciences deals with the question of how a chemical information can be translated into percepts. This is known as the stimulus-percept problem. Here, we set out to better understand this issue by combining knowledge about the chemistry and cognition of smells with computational olfaction. We also assumed that not only one, but several physicochemical models may describe a given olfactory quality. To achieve this aim, a first challenge was to set up a database with ~1700 molecules characterized by chemical features and described by olfactory qualities (e.g. fruity, woody). A second challenge consisted in developing a computational model enabling the discrimination of olfactory qualities based on these chemical features. By meeting these 2 challenges, we provided for several olfactory qualities new chemical models describing why an odorant molecule smells fruity or woody (among others). For most qualities, multiple (rather than a single) chemical models were generated. These findings provide new elements of knowledge about the relationship between odorant chemistry and perception. They also make it possible to envisage concrete applications in the aroma and fragrance field where chemical characterization of smells is an important step in the design of new products.
- Published
- 2019