Flavonoid as a Potent Antioxidant: Quantitative Structure-Activity Relationship Analysis, Mechanism Study, and Molecular Design by Synergizing Molecular Simulation and Machine Learning.

In this work, a quantitative structure-antioxidant activity relationship of flavonoids was performed using a machine learning (ML) method. To achieve lipid-soluble, highly antioxidant flavonoids, 398 molecular structures with various substitute groups were designed based on the flavonoid skeleton. The hydrogen dissociation energies (ΔG1, ΔG2, and ΔG3) related to multiple hydrogen atom transfer processes and the solubility parameter (δ) of flavonoids were calculated using molecular simulation. The group decomposition results and the calculated antioxidant parameters constituted the ML data set. The artificial neural network and random forest models were constructed to predict and analyze the contribution of the substitute groups and positions to the antioxidant activity. The results showed the hydroxyl group at positions B4', B5', and B6' and the branched alkyl group at position C3 in the flavonoid skeleton were the optimal choice for improving antioxidant activity and compatibility with apolar organic materials. Compared to the pyrogallol group-grafted flavonoid, the designed potent flavonoid decreased ΔG1 and δ by 2.2 and 15.1%, respectively, while ΔG2 and ΔG3 kept the favorable lower values. These findings suggest that an efficient flavonoid prefers multiple ortho-phenolic hydroxyl groups and suitable sites with hydrophobic groups. The combination of molecular simulation and the ML method may offer a new research approach for the molecular design of novel antioxidants.

Effect of caffeic acid esters on antioxidant activity and oxidative stability of sunflower oil: Molecular simulation and experiments.

Polyphenol, though used as antioxidants in food industry, suffers from poor solubility issues in vegetable oil. Usually, its solubility would be enhanced through esterification. This work investigated the antioxidant activity and oxidative stability of caffeic acid (CA) and its derivative modified esters by molecular simulation and experiments. Density functional theory (DFT) and molecular dynamic analysis revealed the antioxidant mechanism of CA esters attributing to the comprehensive effects. The lower hydrogen dissociation energy (ΔG) of CA esters with catechol moiety caused the transformation of antioxidant into quinone via the double hydrogen atom transfer reaction. Particularly, the second reduced hydrogen dissociation energy was the keypoint. The strong non-bond energy and hydrogen bond allowed CA esters and oil molecules to interact more efficiently. Hence, the ester moieties enhanced the antioxidant activity with 4.5-6.5 % ΔG reduction compared to CA. Rancimat and DSC assays validated the theoretical predictions. This result shows that the antioxidant activity of CA and its esters could be predicted by this molecular simulation way, which may aid in designing of new polyphenol antioxidant structure.