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A QSTR-Based Expert System to Predict Sweetness of Molecules
This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operati...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5524730/ https://www.ncbi.nlm.nih.gov/pubmed/28791285 http://dx.doi.org/10.3389/fchem.2017.00053 |
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author | Rojas, Cristian Todeschini, Roberto Ballabio, Davide Mauri, Andrea Consonni, Viviana Tripaldi, Piercosimo Grisoni, Francesca |
author_facet | Rojas, Cristian Todeschini, Roberto Ballabio, Davide Mauri, Andrea Consonni, Viviana Tripaldi, Piercosimo Grisoni, Francesca |
author_sort | Rojas, Cristian |
collection | PubMed |
description | This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners. |
format | Online Article Text |
id | pubmed-5524730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-55247302017-08-08 A QSTR-Based Expert System to Predict Sweetness of Molecules Rojas, Cristian Todeschini, Roberto Ballabio, Davide Mauri, Andrea Consonni, Viviana Tripaldi, Piercosimo Grisoni, Francesca Front Chem Chemistry This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners. Frontiers Media S.A. 2017-07-25 /pmc/articles/PMC5524730/ /pubmed/28791285 http://dx.doi.org/10.3389/fchem.2017.00053 Text en Copyright © 2017 Rojas, Todeschini, Ballabio, Mauri, Consonni, Tripaldi and Grisoni. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Chemistry Rojas, Cristian Todeschini, Roberto Ballabio, Davide Mauri, Andrea Consonni, Viviana Tripaldi, Piercosimo Grisoni, Francesca A QSTR-Based Expert System to Predict Sweetness of Molecules |
title | A QSTR-Based Expert System to Predict Sweetness of Molecules |
title_full | A QSTR-Based Expert System to Predict Sweetness of Molecules |
title_fullStr | A QSTR-Based Expert System to Predict Sweetness of Molecules |
title_full_unstemmed | A QSTR-Based Expert System to Predict Sweetness of Molecules |
title_short | A QSTR-Based Expert System to Predict Sweetness of Molecules |
title_sort | qstr-based expert system to predict sweetness of molecules |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5524730/ https://www.ncbi.nlm.nih.gov/pubmed/28791285 http://dx.doi.org/10.3389/fchem.2017.00053 |
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