Predicting the reaction rates between flavonoids and methylglyoxal by combining molecular properties and machine learning

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Predicting the reaction rates between flavonoids and methylglyoxal by combining molecular properties and machine learning. / Zhu, Hongkai; Liu, Jingyuan; Andersen, Mogens L.; Peters, Günther H.J.; Lund, Marianne N.

I: Food Bioscience, Bind 54, 102890, 2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Zhu, H, Liu, J, Andersen, ML, Peters, GHJ & Lund, MN 2023, 'Predicting the reaction rates between flavonoids and methylglyoxal by combining molecular properties and machine learning', Food Bioscience, bind 54, 102890. https://doi.org/10.1016/j.fbio.2023.102890

APA

Zhu, H., Liu, J., Andersen, M. L., Peters, G. H. J., & Lund, M. N. (2023). Predicting the reaction rates between flavonoids and methylglyoxal by combining molecular properties and machine learning. Food Bioscience, 54, [102890]. https://doi.org/10.1016/j.fbio.2023.102890

Vancouver

Zhu H, Liu J, Andersen ML, Peters GHJ, Lund MN. Predicting the reaction rates between flavonoids and methylglyoxal by combining molecular properties and machine learning. Food Bioscience. 2023;54. 102890. https://doi.org/10.1016/j.fbio.2023.102890

Author

Zhu, Hongkai ; Liu, Jingyuan ; Andersen, Mogens L. ; Peters, Günther H.J. ; Lund, Marianne N. / Predicting the reaction rates between flavonoids and methylglyoxal by combining molecular properties and machine learning. I: Food Bioscience. 2023 ; Bind 54.

Bibtex

@article{5bd12f9941d44d65be781cf937bc18f3,
title = "Predicting the reaction rates between flavonoids and methylglyoxal by combining molecular properties and machine learning",
abstract = "The kinetics of the reaction between methylglyoxal (MGO) and epigallocatechin gallate have been investigated at pH 7.4 and 37 °C, and the kinetic data were combined with previously obtained data of six other flavonoids to develop a model that allows to predict the trapping capacity of MGO based on the molecular properties of the seven flavonoids. The observed data were augmented by using synthetic minority oversampling technique forming a new data set that was used to create the predicting models for the trapping rate constant of MGO by flavonoids via principal component regression (PCR) and back-propagation neural network algorithm, respectively. The PCR model based on the first six principle components was robust and accurate comparing other created models, with an associated root-mean-square error value of 8.02 × 10−7 on the testing set. This work provides quantitative structure-activity models for rapid and accurate prediction of the trapping rate constant of MGO by flavonoids.",
keywords = "Computational chemistry, Data augmentation, Dicarbonyl, Kinetics, Neural network, Principal component regression",
author = "Hongkai Zhu and Jingyuan Liu and Andersen, {Mogens L.} and Peters, {G{\"u}nther H.J.} and Lund, {Marianne N.}",
note = "Publisher Copyright: {\textcopyright} 2023 The Authors",
year = "2023",
doi = "10.1016/j.fbio.2023.102890",
language = "English",
volume = "54",
journal = "Food Bioscience",
issn = "2212-4292",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Predicting the reaction rates between flavonoids and methylglyoxal by combining molecular properties and machine learning

AU - Zhu, Hongkai

AU - Liu, Jingyuan

AU - Andersen, Mogens L.

AU - Peters, Günther H.J.

AU - Lund, Marianne N.

N1 - Publisher Copyright: © 2023 The Authors

PY - 2023

Y1 - 2023

N2 - The kinetics of the reaction between methylglyoxal (MGO) and epigallocatechin gallate have been investigated at pH 7.4 and 37 °C, and the kinetic data were combined with previously obtained data of six other flavonoids to develop a model that allows to predict the trapping capacity of MGO based on the molecular properties of the seven flavonoids. The observed data were augmented by using synthetic minority oversampling technique forming a new data set that was used to create the predicting models for the trapping rate constant of MGO by flavonoids via principal component regression (PCR) and back-propagation neural network algorithm, respectively. The PCR model based on the first six principle components was robust and accurate comparing other created models, with an associated root-mean-square error value of 8.02 × 10−7 on the testing set. This work provides quantitative structure-activity models for rapid and accurate prediction of the trapping rate constant of MGO by flavonoids.

AB - The kinetics of the reaction between methylglyoxal (MGO) and epigallocatechin gallate have been investigated at pH 7.4 and 37 °C, and the kinetic data were combined with previously obtained data of six other flavonoids to develop a model that allows to predict the trapping capacity of MGO based on the molecular properties of the seven flavonoids. The observed data were augmented by using synthetic minority oversampling technique forming a new data set that was used to create the predicting models for the trapping rate constant of MGO by flavonoids via principal component regression (PCR) and back-propagation neural network algorithm, respectively. The PCR model based on the first six principle components was robust and accurate comparing other created models, with an associated root-mean-square error value of 8.02 × 10−7 on the testing set. This work provides quantitative structure-activity models for rapid and accurate prediction of the trapping rate constant of MGO by flavonoids.

KW - Computational chemistry

KW - Data augmentation

KW - Dicarbonyl

KW - Kinetics

KW - Neural network

KW - Principal component regression

U2 - 10.1016/j.fbio.2023.102890

DO - 10.1016/j.fbio.2023.102890

M3 - Journal article

AN - SCOPUS:85164004588

VL - 54

JO - Food Bioscience

JF - Food Bioscience

SN - 2212-4292

M1 - 102890

ER -

ID: 360068263