Literature DB >> 28129594

Importance of hydrogen bonding and aromaticity indices in QSAR modeling of the antioxidative capacity of selected (poly)phenolic antioxidants.

Svetlana Jeremić1, Slavko Radenković2, Miloš Filipović3, Marija Antić2, Ana Amić4, Zoran Marković3.   

Abstract

The quantitative structure-activity relationship (QSAR) models for predicting antioxidative capacity of 21 structurally similar natural and synthetic phenolic antioxidants was considered. The one-, two- and three-descriptor QSAR models were developed. For this purpose the literature data on the vitamin C equivalent antioxidative capacity (VCEAC) values were used as experimental descriptor of antioxidative capacity. Some thermodynamic and aromaticity properties, as well as the natural bond analysis (NBO) based quantities aimed at measuring the strength of intramolecular hydrogen bonds, were used as independent variables. It was examined whether a combination of these variables can yield a mathematical function that is in good correlation with the VCEAC values. It was shown that a combination of a certain thermodynamic descriptor (related to the single proton loss electron transfer mechanism) with the NBO-based quantities results in several two-descriptor models with the correlation coefficient greater than 0.950. Thus, a significant influence of internal hydrogen bonds on the antioxidative capacity of the studied molecules was confirmed. The best correlation with the VCEAC values was achieved within a three-descriptor QSAR model. This model was obtained by including a magnetic aromaticity index. It was found that aromaticity has only secondary effects on the antioxidative capacity.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Antioxidative activity; Hydrogen bond energy; Phenolic compounds; QSAR; Thermodynamic parameters

Mesh:

Substances:

Year:  2017        PMID: 28129594     DOI: 10.1016/j.jmgm.2017.01.011

Source DB:  PubMed          Journal:  J Mol Graph Model        ISSN: 1093-3263            Impact factor:   2.518


  3 in total

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2.  Hybrid Classification/Regression Approach to QSAR Modeling of Stoichiometric Antiradical Capacity Assays' Endpoints.

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Journal:  Molecules       Date:  2022-03-24       Impact factor: 4.411

3.  QSPR Modeling and Experimental Determination of the Antioxidant Activity of Some Polycyclic Compounds in the Radical-Chain Oxidation Reaction of Organic Substrates.

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  3 in total

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