Cargando…
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...
Autores principales: | , |
---|---|
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 |
_version_ | 1784769098741186560 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9381526 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT sainikushagra predictingodorfrommolecularstructureamultilabelclassificationapproach AT ramanathanvenkatnarayan predictingodorfrommolecularstructureamultilabelclassificationapproach |