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Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts
A series of pyrrole derivatives and their antioxidant scavenging activities toward the superoxide anion (O(2)(•−)), hydroxyl radical ((•)OH), and 1,1-diphenyl-2-picryl-hydrazyl (DPPH(•)) served as the training data sets of a quantitative structure–activity relationship (QSAR) study. The steric and e...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959680/ https://www.ncbi.nlm.nih.gov/pubmed/36838583 http://dx.doi.org/10.3390/molecules28041596 |
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author | Xie, Wanting Wiriyarattanakul, Sopon Rungrotmongkol, Thanyada Shi, Liyi Wiriyarattanakul, Amphawan Maitarad, Phornphimon |
author_facet | Xie, Wanting Wiriyarattanakul, Sopon Rungrotmongkol, Thanyada Shi, Liyi Wiriyarattanakul, Amphawan Maitarad, Phornphimon |
author_sort | Xie, Wanting |
collection | PubMed |
description | A series of pyrrole derivatives and their antioxidant scavenging activities toward the superoxide anion (O(2)(•−)), hydroxyl radical ((•)OH), and 1,1-diphenyl-2-picryl-hydrazyl (DPPH(•)) served as the training data sets of a quantitative structure–activity relationship (QSAR) study. The steric and electronic descriptors obtained from quantum chemical calculations were related to the three O(2)(•−), (•)OH, and DPPH(•) scavenging activities using the genetic algorithm combined with multiple linear regression (GA-MLR) and artificial neural networks (ANNs). The GA-MLR models resulted in good statistical values; the coefficient of determination (R(2)) of the training set was greater than 0.8, and the root mean square error (RMSE) of the test set was in the range of 0.3 to 0.6. The main molecular descriptors that play an important role in the three types of antioxidant activities are the bond length, HOMO energy, polarizability, and AlogP. In the QSAR-ANN models, a good R(2) value above 0.9 was obtained, and the RMSE of the test set falls in a similar range to that of the GA-MLR models. Therefore, both the QSAR GA-MLR and QSAR-ANN models were used to predict the newly designed pyrrole derivatives, which were developed based on their starting reagents in the synthetic process. |
format | Online Article Text |
id | pubmed-9959680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99596802023-02-26 Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts Xie, Wanting Wiriyarattanakul, Sopon Rungrotmongkol, Thanyada Shi, Liyi Wiriyarattanakul, Amphawan Maitarad, Phornphimon Molecules Article A series of pyrrole derivatives and their antioxidant scavenging activities toward the superoxide anion (O(2)(•−)), hydroxyl radical ((•)OH), and 1,1-diphenyl-2-picryl-hydrazyl (DPPH(•)) served as the training data sets of a quantitative structure–activity relationship (QSAR) study. The steric and electronic descriptors obtained from quantum chemical calculations were related to the three O(2)(•−), (•)OH, and DPPH(•) scavenging activities using the genetic algorithm combined with multiple linear regression (GA-MLR) and artificial neural networks (ANNs). The GA-MLR models resulted in good statistical values; the coefficient of determination (R(2)) of the training set was greater than 0.8, and the root mean square error (RMSE) of the test set was in the range of 0.3 to 0.6. The main molecular descriptors that play an important role in the three types of antioxidant activities are the bond length, HOMO energy, polarizability, and AlogP. In the QSAR-ANN models, a good R(2) value above 0.9 was obtained, and the RMSE of the test set falls in a similar range to that of the GA-MLR models. Therefore, both the QSAR GA-MLR and QSAR-ANN models were used to predict the newly designed pyrrole derivatives, which were developed based on their starting reagents in the synthetic process. MDPI 2023-02-07 /pmc/articles/PMC9959680/ /pubmed/36838583 http://dx.doi.org/10.3390/molecules28041596 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xie, Wanting Wiriyarattanakul, Sopon Rungrotmongkol, Thanyada Shi, Liyi Wiriyarattanakul, Amphawan Maitarad, Phornphimon Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts |
title | Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts |
title_full | Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts |
title_fullStr | Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts |
title_full_unstemmed | Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts |
title_short | Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts |
title_sort | rational design of a low-data regime of pyrrole antioxidants for radical scavenging activities using quantum chemical descriptors and qsar with the ga-mlr and ann concepts |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959680/ https://www.ncbi.nlm.nih.gov/pubmed/36838583 http://dx.doi.org/10.3390/molecules28041596 |
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