Deciphering antioxidant interactions via data mining and RDKit - Lucas B. Ayres; Justin T. Furgala; Carlos D. Garcia (Scientific Reports)
Abstract Minimizing the oxidation of lipids remains one of the most important challenges to extend the shelf-life of food products and reduce food waste. While most consumer products contain antioxidants, the most efficient strategy is to incorporate combinations of two or more compounds, boosting the total antioxidant capacity. Unfortunately, the reasons for observing synergistic / antagonistic / additive effects in food samples are still unclear, and it is common to observe very different responses even for similar mixtures. Aiming to identify chemical features that can be correlated with specific responses, this report presents an analysis of 1243 mixtures of antioxidants reported in the literature. The analysis focuses on the most commonly reported compounds and mixtures and considers how various chemical descriptors (number of atoms, number of heavy atoms, number of heteroatoms, number of carbon atoms, number of oxygen atoms, number of nitrogen atoms, number of chloride atoms, polar surface area, molecular weight, number of aromatic rings, logP, and hydrogen bond counts) affect the response. Out of those, our analysis showed that hydrogen bonding plays an important role in determining how antioxidants interact, potentially affecting the overall behavior of mixtures. Far from drawing a universal conclusion about one particular mechanism; this article provides an overview of what has worked so far, delving into the possible chemical variables behind those interactions.