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Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Research Article

Probing into the Molecular Requirements for Antioxidant Activity in Plant Phenolic Compounds Utilizing a Combined Strategy of PCA and ANN

Author(s): Snezana Agatonovic-Kustrin, David W. Morton and Petar Ristivojevic

Volume 20, Issue 1, 2017

Page: [25 - 34] Pages: 10

DOI: 10.2174/1386207320666170102123146

Price: $65

Abstract

Aim and Objective: This study investigates molecular structural requirements that are responsible for the antioxidant activity in phenolic compounds.

Method: Antioxidant activity of compounds was determined with a 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical assay. Principal component analysis (PCA) was used to classify phenolic antioxidants according to the key molecular features that contribute to their antioxidant activity. Artificial neutral networks (ANNs) was used to develop a predictive QSAR model.

Results: Both models agreed that structural characteristics of phenolic compounds responsible for the antioxidant activity include: (1) number and position of alcohol groups on the aromatic ring; (2) molecular size; (3) flexibility/bulkiness; and (4) water solubility. PCA has classified data into phenolic acids and flavonoids, suggesting two distinct mechanisms of action. ANN has confirmed different mechanisms of action for flavonoids and polyphenolic acids, i.e. breaking of free radical chain reactions by donation of a hydrogen atom to neutralise a free radical and the chelating ability of polyphenolic acids.

Conclusion: Although two phenolic acids may have the same relative polarity, their different functional groups may drastically change the nature of their interactions with free radicals, and their antioxidant activity.

Keywords: Artificial neural networks, flavonoids, free radical scavenging, metal chelation, molecular descriptors, polyphenolic acids, principle component analysis.


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