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Functional odor classification through a medicinal chemistry approach

Crucial for any hypothesis about odor coding is the classification and prediction of sensory qualities in chemical compounds. The relationship between perceptual quality and molecular structure has occupied olfactory scientists throughout the 20th century, but details of the mechanism remain elusive...

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Autores principales: Poivet, Erwan, Tahirova, Narmin, Peterlin, Zita, Xu, Lu, Zou, Dong-Jing, Acree, Terry, Firestein, Stuart
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5817921/
https://www.ncbi.nlm.nih.gov/pubmed/29487905
http://dx.doi.org/10.1126/sciadv.aao6086
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author Poivet, Erwan
Tahirova, Narmin
Peterlin, Zita
Xu, Lu
Zou, Dong-Jing
Acree, Terry
Firestein, Stuart
author_facet Poivet, Erwan
Tahirova, Narmin
Peterlin, Zita
Xu, Lu
Zou, Dong-Jing
Acree, Terry
Firestein, Stuart
author_sort Poivet, Erwan
collection PubMed
description Crucial for any hypothesis about odor coding is the classification and prediction of sensory qualities in chemical compounds. The relationship between perceptual quality and molecular structure has occupied olfactory scientists throughout the 20th century, but details of the mechanism remain elusive. Odor molecules are typically organic compounds of low molecular weight that may be aliphatic or aromatic, may be saturated or unsaturated, and may have diverse functional polar groups. However, many molecules conforming to these characteristics are odorless. One approach recently used to solve this problem was to apply machine learning strategies to a large set of odors and human classifiers in an attempt to find common and unique chemical features that would predict a chemical’s odor. We use an alternative method that relies more on the biological responses of olfactory sensory neurons and then applies the principles of medicinal chemistry, a technique widely used in drug discovery. We demonstrate the effectiveness of this strategy through a classification for esters, an important odorant for the creation of flavor in wine. Our findings indicate that computational approaches that do not account for biological responses will be plagued by both false positives and false negatives and fail to provide meaningful mechanistic data. However, the two approaches used in tandem could resolve many of the paradoxes in odor perception.
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spelling pubmed-58179212018-02-27 Functional odor classification through a medicinal chemistry approach Poivet, Erwan Tahirova, Narmin Peterlin, Zita Xu, Lu Zou, Dong-Jing Acree, Terry Firestein, Stuart Sci Adv Research Articles Crucial for any hypothesis about odor coding is the classification and prediction of sensory qualities in chemical compounds. The relationship between perceptual quality and molecular structure has occupied olfactory scientists throughout the 20th century, but details of the mechanism remain elusive. Odor molecules are typically organic compounds of low molecular weight that may be aliphatic or aromatic, may be saturated or unsaturated, and may have diverse functional polar groups. However, many molecules conforming to these characteristics are odorless. One approach recently used to solve this problem was to apply machine learning strategies to a large set of odors and human classifiers in an attempt to find common and unique chemical features that would predict a chemical’s odor. We use an alternative method that relies more on the biological responses of olfactory sensory neurons and then applies the principles of medicinal chemistry, a technique widely used in drug discovery. We demonstrate the effectiveness of this strategy through a classification for esters, an important odorant for the creation of flavor in wine. Our findings indicate that computational approaches that do not account for biological responses will be plagued by both false positives and false negatives and fail to provide meaningful mechanistic data. However, the two approaches used in tandem could resolve many of the paradoxes in odor perception. American Association for the Advancement of Science 2018-02-09 /pmc/articles/PMC5817921/ /pubmed/29487905 http://dx.doi.org/10.1126/sciadv.aao6086 Text en Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (http://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Poivet, Erwan
Tahirova, Narmin
Peterlin, Zita
Xu, Lu
Zou, Dong-Jing
Acree, Terry
Firestein, Stuart
Functional odor classification through a medicinal chemistry approach
title Functional odor classification through a medicinal chemistry approach
title_full Functional odor classification through a medicinal chemistry approach
title_fullStr Functional odor classification through a medicinal chemistry approach
title_full_unstemmed Functional odor classification through a medicinal chemistry approach
title_short Functional odor classification through a medicinal chemistry approach
title_sort functional odor classification through a medicinal chemistry approach
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5817921/
https://www.ncbi.nlm.nih.gov/pubmed/29487905
http://dx.doi.org/10.1126/sciadv.aao6086
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