Search

The study employed the random forest (RF) and artificial neural network (ANN) methods based on the quantitative structure–activity relationship (QSAR) techniques to analyze NSAIDs-Se derivatives and their antioxidant abilities. The best predictive models by the RF method yielded a coefficient of determination (R2) value of 0.868 for the training set, and the root mean square error (RMSE) of the test set was only 0.053. For the QSAR-ANN, the best predictive models resulted in an R2 value of 0.935, and the RMSE of the test set was 0.068. Based on the best models, the steric, electrostatic, and enthalpy descriptors are found to be related to antioxidant prediction. Thus, to extend the predictive ability of the obtained QSAR-ML models, an external set was collected from a later publication of NSAIDs-Se derivatives with experimental antioxidant abilities. The efficacy of two QSAR models in forecasting the antioxidant abilities of an external set of NSAIDs-Se derivatives was evaluated. The QSAR with machine learning models demonstrated high efficiency in predicting the antioxidant abilities of the external NSAIDs-Se set with an RMSE of the external set in the range of 0.074–0.087. Therefore, the results suggest that fine-tuning machine learning-based QSAR studies can aid in the design of novel NSAIDs-Se derivatives with highly efficient antioxidant prediction. [ABSTRACT FROM AUTHOR]

The quantitative structure–electrochemistry relationship (QSER) method was applied to a series of transition-metal-coordinated porphyrins to relate their structural properties to their electrochemical CO2 reduction activity. Since the reactions mainly occur within the core of the metalloporphyrin catalysts, the cluster model was used to calculate their structural and electronic properties using density functional theory with the M06L exchange–correlation functional. Three dependent variables were employed in this work: the Gibbs free energies of H*, C*OOH, and O*CHO. QSER, with the genetic algorithm combined with multiple linear regression (GA–MLR), was used to manipulate the mathematical models of all three Gibbs free energies. The obtained statistical values resulted in a good predictive ability (R2 value) greater than 0.945. Based on our QSER models, both the electronic properties (charges of the metal and porphyrin) and the structural properties (bond lengths between the metal center and the nitrogen atoms of the porphyrin) play a significant role in the three Gibbs free energies. This finding was further applied to estimate the CO2 reduction activities of the metal–monoamino–porphyrins, which will prove beneficial in further experimental developments. [ABSTRACT FROM AUTHOR]

A series of pyrrole derivatives and their antioxidant scavenging activities toward the superoxide anion (O2•−), 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 O2•−, •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 (R2) 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 R2 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. [ABSTRACT FROM AUTHOR]