Quantitative structure-activity relationship study for prediction of antifungal properties of phenolic compounds

被引:8
作者
Appell, Michael [1 ]
Tu, Yi-Shu [1 ,2 ]
Compton, David L. [3 ]
Evans, Kervin O. [3 ]
Wang, Lijuan C. [1 ]
机构
[1] ARS, Mycotoxin Prevent & Appl Microbiol Res Unit, USDA, Natl Ctr Agr Utilizat Res, 1815 N Univ St, Peoria, IL 61604 USA
[2] Insil Taiwan Inc, 17F 3 Pk St Nangang Dist, Taipei 11503, Taiwan
[3] ARS, Renewable Prod Technol Res Unit, USDA, Natl Ctr Agr Utilizat Res, 1815 N Univ St, Peoria, IL 61604 USA
关键词
Food safety; Machine learning; QSAR; Mycotoxin; Fungicide; QSAR; VALIDATION; THYMOL; MODELS;
D O I
10.1007/s11224-020-01549-1
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Antifungal compounds are of interest to reduce commodity spoilage and exposure to mycotoxins. In this study, a series of quantitative structure-activity relationship (QSAR) equations based on topological properties were developed to gain insight into the antifungal activities of phenolic compounds. The molecules were geometry optimized using B3LYP/6-311+G** density functional theory calculations. Analysis of the frontier orbital properties revealed that conjugated phenolic compounds possessed smaller band gap energies. Genetic function approximation (GFA) on populations of 100 one to two descriptor models over 10,000 generations identified several models for antifungal activity against Fusarium verticillioides, Fusarium oxysporum, Aspergillus flavus, Aspergillus fumigatus, Penicillium expansum, and Penicillium brevicompactum. Phenolic compounds with greater antifungal activity possessed a lower electrophilicity index. The correlation coefficients for the one and two descriptor models ranged from 0.627 to 0.790 and 0.762 to 0.939, respectively. Molecular descriptors associated with electrostatic and topological properties are important to describe the antifungal activities of the phenolic compounds studied.
引用
收藏
页码:1621 / 1630
页数:10
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