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(center dot-)), hydroxyl radical ((OH)-O-center dot), and 1,1-diphenyl-2-picryl-hydrazyl (DPPH center dot) 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(center dot-), (OH)-O-center dot, and DPPH center dot 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.

Automated summary

A series of pyrrole derivatives were studied using QSAR in order to investigate their antioxidant scavenging activities. The GA-MLR and QSAR-ANN models were used to establish relationships between the molecular descriptors and the scavenging activities. Both models showed good predictive abilities, with high R-2 values and low RMSE values in the test set. The key molecular descriptors for the antioxidant activities were bond length, HOMO energy, polarizability, and AlogP.