Feature Selection for the Interpretation of Antioxidant Mechanisms in Plant Phenolics

Abstract

Antioxidants, represented by plant phenolics, protect living tissues by scavenging reactive oxygen species through diverse reaction mechanisms. Research on antioxidants is often individualized, for example, focusing on the evaluation of their activity against a single reactive oxygen species or examining the antioxidant properties of compounds with similar structures. In this study, multivariate analysis was used to comprehensively examine antioxidant properties. Eighteen features were selected to explain the results of the antioxidant capacity tests. These selected features were then evaluated by supervised learning, using the results of the antioxidant capacity assays. Dimension-reduction techniques were also used to represent the compound space with antioxidants as a two-dimensional distribution. A small amount of data obtained from several assays provided us with comprehensive information on the relationships between the structures and activities of antioxidants.

DPPH Measurements and Structure—Activity Relationship Studies on the Antioxidant Capacity of Phenols

“…Similarly, for substituents at the o-position, the order of activity was as follows: methoxy (entry 19) > 1000 µg/mL > H, carbonyl, carboxy, alkyl. For compounds with one methoxy group at the o-position and different substituents at the p-position, the TEACs were in the following order: alkyl > H > carbonyl (entries 22,19,24). For the methoxy group at the 2,6position, the substituents at the p-position were also in the order of alkyl > H > carbonyl.…”

“…In addition, the ionization po tential (IP), proton dissociation enthalpy (PDE), bond dissociation enthalpy (BDE), proton affinity (PA), and electron transfer enthalpy (ETE) are considered to play roles in these mechanisms, according to some studies [16,17]. Our study also uses these indicators to predict the values obtained in antioxidant tests through machine learning [18,19]. PT is a two-step reaction involving the transfer of one electron followed by the donation of a proton HAT involves the transfer of a hydrogen atom.…”

SALSA: Semantically-Aware Latent Space Autoencoder

“…We encode a sample of 2000 molecules into SALSA, Naive, and Contra space, and from those latent representations, compute the EuD between each pair for all three models. Following, we calculate 10 physicochemical properties (chosen for their relevance to drug discovery) for each molecule using RDKit (Fujimoto and Gotoh 2023;Wei et al 2020). As an illustrative example, Figure 5 For each property, we compute the property difference (Prop∆) between each molecular pair.…”

Rosemary: Unrevealing an old aromatic crop as a new source of promising functional food additive—A review

“…Plants are continuously served as one of the most predominant bioresources with an outstanding ability to produce structurally novel natural compounds and biologically meaningful nutritional compositions, which were profoundly exemplified by essential oils (Murray et al., 2019), flavonoids (Tiwari & Mishra, 2023), phenolic acids (Jiang et al., 2022), and so on. The secondary metabolites in plants usually exhibit pharmaceutically significant antibacterial (Pormohammad et al., 2022), antitumor (Pinto, Cidade et al., 2021), antioxidant (Fujimoto & Gotoh, 2023), insecticidal (Chen et al., 2021), hypoglycemic (Abdelrahman et al., 2020; Long et al., 2022), and neuroprotective (Yang et al., 2019; Zhang et al., 2020) activities. Aromatic plants are highly regarded as strategic fragrance resources with abundant aromatic components that share promisingly significant potential medicinal benefits, nutritional functions, and spice values (Pinto, Aires et al., 2021).…”