Chemical features mining provides new descriptive structure-odor relationships

Carmen C Licon,Moustafa Bensafi,Marylou Mantel,Mohammed Sabri,Mehdi Kaytoue,Marc Plantevit,Jerome Golebiowski,Arnaud Fournel,Caroline Bushdid,Celine Robardet,Guillaume Bosc

doi:10.1371/journal.pcbi.1006945

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.

Highlights

Around the turn of the century, with its acknowledgement as an object of science by the Nobel society [1] the hidden sense associated with the perception of odorant chemicals, hitherto considered superfluous to cognition, became a focus of study in its own right
An important issue in olfaction sciences deals with the question of how a chemical information can be translated into percepts
A first challenge was to set up a database with ~1700 molecules characterized by chemical features and described by olfactory qualities

Summary

Introduction

Around the turn of the century, with its acknowledgement as an object of science by the Nobel society [1] the hidden sense associated with the perception of odorant chemicals, hitherto considered superfluous to cognition, became a focus of study in its own right. Neuroscientific studies have revealed that odor perception is the consequence of a complex phenomenon rooted in the chemical properties of a volatile molecule (described by multiple physicochemical descriptors) further detected by our olfactory receptors in the nasal cavity [6]. A neural signal is transmitted to central olfactory brain structures [7] At this stage, a complete neural representation, called “odor” is generated and it can be described semantically by various types of perceptual qualities (e.g., musky, fruity, floral, woody etc.). In addition to the hedonic valence of odors, others have looked for predictive models describing odor perception and quality (see [11,12,13,14]) This was the aim of a crowd-sourced challenge recently proposed by IBM Research and Sage called DREAM Olfaction Prediction Challenge. The challenge resulted in several models that were able to predict pleasantness and intensity as well as 8 out of 19 semantic descriptors (namely “garlic”, “fish”, “sweet”, “fruit”, “burnt”, “spices”, “flower” and “sour”) with an average correlation of predictions across all models above 0.5 [15]

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