QSAR modeling of antiradical properties of phenolic compounds using DFT calculations

IF 2.4 Q3 CHEMISTRY, MULTIDISCIPLINARY Moroccan Journal of Chemistry Pub Date : 2020-07-02 DOI:10.48317/IMIST.PRSM/MORJCHEM-V8I4.20579
H. Lafridi, A. Oussa, H. Zgou, M. Bouachrine
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引用次数: 2

Abstract

This paper describes a quantitative structure – activity relationship study of the antiradical properties of 31 flavonoids belonging to different groups such as: flavonols, flavonones, dihydroflavonols and biflavonones. Using density functional theory (DFT) calculations, some structural characteristics such as frontier molecular orbitals, molecular descriptors, have been studied. To gain insights into the chemical structure and property of the studied compounds, many types of descriptors are generated by using DFT/B3LYP 6-31G(d,p) and other software. Also, The Principle Component Analysis (PCA), Multiple Linear and Nonlinear Regression (MLR and MNLR), and Artificial Neural Network (ANN) have been investigated to select the descriptors, and to generate the correlation models that relate the structural feature to the biological activity. The statistical results of the MLR, MNLR, and ANN indicate that the determination coefficient R 2 were 0.811, 0.646, 0.982, respectively. A good correlation coefficient is obtained, and the antiradical activities of these compounds are well predicted. These models are expected to be useful for screening of polyphenolic antioxidants.
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基于DFT计算的酚类化合物抗自由基性质的QSAR建模
本文对黄酮醇、黄酮酮、二氢黄酮醇和双黄酮酮等31种黄酮类化合物的抗自由基性质进行了定量构效关系研究。利用密度泛函理论(DFT)计算,研究了一些结构特征,如前沿分子轨道、分子描述符。为了深入了解所研究化合物的化学结构和性质,使用DFT/B3LYP 6-31G(d,p)和其他软件生成了许多类型的描述符。此外,还研究了主成分分析(PCA)、多元线性和非线性回归(MLR和MNLR)以及人工神经网络(ANN)来选择描述符,并生成将结构特征与生物活性联系起来的相关模型。MLR、MNLR和ANN的统计结果表明,决定系数R2分别为0.811、0.646和0.982。获得了良好的相关系数,并很好地预测了这些化合物的抗自由基活性。这些模型有望用于筛选多酚类抗氧化剂。
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来源期刊
Moroccan Journal of Chemistry
Moroccan Journal of Chemistry CHEMISTRY, MULTIDISCIPLINARY-
CiteScore
3.40
自引率
9.10%
发文量
0
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