Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules
Abstract Deciphering the relationship between molecules, olfactory receptors (ORs) and corresponding odors remains a challenging task. It requires a comprehensive identification of ORs responding to a given odorant. With the recent advances in artificial intelligence and the growing research in deco...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-23176-y |
| _version_ | 1798044354122612736 |
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| author | Rayane Achebouche Anne Tromelin Karine Audouze Olivier Taboureau |
| author_facet | Rayane Achebouche Anne Tromelin Karine Audouze Olivier Taboureau |
| author_sort | Rayane Achebouche |
| collection | DOAJ |
| description | Abstract Deciphering the relationship between molecules, olfactory receptors (ORs) and corresponding odors remains a challenging task. It requires a comprehensive identification of ORs responding to a given odorant. With the recent advances in artificial intelligence and the growing research in decoding the human olfactory perception from chemical features of odorant molecules, the applications of advanced machine learning have been revived. In this study, Convolutional Neural Network (CNN) and Graphical Convolutional Network (GCN) models have been developed on odorant molecules-odors and odorant molecules-olfactory receptors using a large set of 5955 molecules, 160 odors and 106 olfactory receptors. The performance of such models is promising with a Precision/Recall Area Under Curve of 0.66 for the odorant-odor and 0.91 for the odorant-olfactory receptor GCN models respectively. Furthermore, based on the correspondence of odors and ORs associated for a set of 389 compounds, an odor-olfactory receptor pairwise score was computed for each odor-OR combination allowing to suggest a combinatorial relationship between olfactory receptors and odors. Overall, this analysis demonstrate that artificial intelligence may pave the way in the identification of the smell perception and the full repertoire of receptors for a given odorant molecule. |
| first_indexed | 2024-04-11T23:02:30Z |
| format | Article |
| id | doaj.art-287bcf5ecf0e421984ebcb22e00b8de8 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T23:02:30Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-287bcf5ecf0e421984ebcb22e00b8de82022-12-22T03:58:05ZengNature PortfolioScientific Reports2045-23222022-11-0112111410.1038/s41598-022-23176-yApplication of artificial intelligence to decode the relationships between smell, olfactory receptors and small moleculesRayane Achebouche0Anne Tromelin1Karine Audouze2Olivier Taboureau3Université Paris Cité, CNRS, INSERM U1133, Unité de Biologie Fonctionnelle et AdaptativeCentre Des Sciences du Goût Et de L’Alimentation, CNRS, INRAE, Institut Agro, Université Bourgogne Franche-ComtéUniversité Paris Cité, T3S, Inserm UMR S-1124Université Paris Cité, CNRS, INSERM U1133, Unité de Biologie Fonctionnelle et AdaptativeAbstract Deciphering the relationship between molecules, olfactory receptors (ORs) and corresponding odors remains a challenging task. It requires a comprehensive identification of ORs responding to a given odorant. With the recent advances in artificial intelligence and the growing research in decoding the human olfactory perception from chemical features of odorant molecules, the applications of advanced machine learning have been revived. In this study, Convolutional Neural Network (CNN) and Graphical Convolutional Network (GCN) models have been developed on odorant molecules-odors and odorant molecules-olfactory receptors using a large set of 5955 molecules, 160 odors and 106 olfactory receptors. The performance of such models is promising with a Precision/Recall Area Under Curve of 0.66 for the odorant-odor and 0.91 for the odorant-olfactory receptor GCN models respectively. Furthermore, based on the correspondence of odors and ORs associated for a set of 389 compounds, an odor-olfactory receptor pairwise score was computed for each odor-OR combination allowing to suggest a combinatorial relationship between olfactory receptors and odors. Overall, this analysis demonstrate that artificial intelligence may pave the way in the identification of the smell perception and the full repertoire of receptors for a given odorant molecule.https://doi.org/10.1038/s41598-022-23176-y |
| spellingShingle | Rayane Achebouche Anne Tromelin Karine Audouze Olivier Taboureau Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules Scientific Reports |
| title | Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules |
| title_full | Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules |
| title_fullStr | Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules |
| title_full_unstemmed | Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules |
| title_short | Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules |
| title_sort | application of artificial intelligence to decode the relationships between smell olfactory receptors and small molecules |
| url | https://doi.org/10.1038/s41598-022-23176-y |
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