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Predicting odor from molecular structure: a multi-label classification approach

Decoding the factors behind odor perception has long been a challenge in the field of human neuroscience, olfactory research, perfumery, psychology, biology and chemistry. The new wave of data-driven and machine learning approaches to predicting molecular properties are a growing area of research in...

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Autores principales: Saini, Kushagra, Ramanathan, Venkatnarayan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381526/
https://www.ncbi.nlm.nih.gov/pubmed/35974078
http://dx.doi.org/10.1038/s41598-022-18086-y
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author Saini, Kushagra
Ramanathan, Venkatnarayan
author_facet Saini, Kushagra
Ramanathan, Venkatnarayan
author_sort Saini, Kushagra
collection PubMed
description Decoding the factors behind odor perception has long been a challenge in the field of human neuroscience, olfactory research, perfumery, psychology, biology and chemistry. The new wave of data-driven and machine learning approaches to predicting molecular properties are a growing area of research interest and provide for significant improvement over conventional statistical methods. We look at these approaches in the context of predicting molecular odor, specifically focusing on multi-label classification strategies employed for the same. Namely binary relevance, classifier chains, and random forests adapted to deal with such a task. This challenge, termed quantitative structure–odor relationship, remains an unsolved task in the field of sensory perception in machine learning, and we hope to emulate the results achieved in the field of vision and auditory perception in olfaction over time.
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spelling pubmed-93815262022-08-18 Predicting odor from molecular structure: a multi-label classification approach Saini, Kushagra Ramanathan, Venkatnarayan Sci Rep Article Decoding the factors behind odor perception has long been a challenge in the field of human neuroscience, olfactory research, perfumery, psychology, biology and chemistry. The new wave of data-driven and machine learning approaches to predicting molecular properties are a growing area of research interest and provide for significant improvement over conventional statistical methods. We look at these approaches in the context of predicting molecular odor, specifically focusing on multi-label classification strategies employed for the same. Namely binary relevance, classifier chains, and random forests adapted to deal with such a task. This challenge, termed quantitative structure–odor relationship, remains an unsolved task in the field of sensory perception in machine learning, and we hope to emulate the results achieved in the field of vision and auditory perception in olfaction over time. Nature Publishing Group UK 2022-08-16 /pmc/articles/PMC9381526/ /pubmed/35974078 http://dx.doi.org/10.1038/s41598-022-18086-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Saini, Kushagra
Ramanathan, Venkatnarayan
Predicting odor from molecular structure: a multi-label classification approach
title Predicting odor from molecular structure: a multi-label classification approach
title_full Predicting odor from molecular structure: a multi-label classification approach
title_fullStr Predicting odor from molecular structure: a multi-label classification approach
title_full_unstemmed Predicting odor from molecular structure: a multi-label classification approach
title_short Predicting odor from molecular structure: a multi-label classification approach
title_sort predicting odor from molecular structure: a multi-label classification approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381526/
https://www.ncbi.nlm.nih.gov/pubmed/35974078
http://dx.doi.org/10.1038/s41598-022-18086-y
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