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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2018
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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. |
format | Online Article Text |
id | pubmed-5817921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
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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