Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants.

In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on N -phenyl-1-naphthylamine and N -phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, including the hydrogen dissociation energy, solubility parameter, and binding energy, were calculated through molecular simulations. Then, a group decomposition scheme was determined to decompose 302 antioxidants. The antioxidant parameters and decomposition results constituted machine-learning data sets. Using an artificial neural network model, a correlation coefficient between the predicted and true values above 0.88 and an average relative error within 6% were achieved. Random forest models were used to analyze the factors affecting antioxidant activity from chemical and physical perspectives; the results showed that amino and alkyl groups were conducive to improving antioxidant performance. Moreover, substituent positions 1, 7, and 10 of N -phenyl-1-naphthylamine and 3, 7, and 10 of N -phenyl-2-naphthylamine were found to be the optimal positions for modifications to improve antioxidant activity. Two potentially efficient phenylnaphthylamine antioxidant structures were proposed and their antioxidant parameters were also calculated; the hydrogen dissociation energy and solubility parameter decreased by more than 9% and 7%, respectively, whereas the binding energy increased by more than 16% compared with the benchmark of N -phenyl-1-naphthylamine. These results indicate that molecular simulation and machine learning could provide alternative tools for the molecular design of new antioxidants.