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

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.