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基于信息熵的灵芝三萜类化合物和甾体类化合物分类

Information entropy-based classification of triterpenoids and steroids from Ganoderma.

作者信息

Castellano Gloria, Torrens Francisco

机构信息

Departamento de Ciencias Experimentales y Matemáticas, Facultad de Veterinaria y Ciencias Experimentales, Universidad Católica de Valencia San Vicente Mártir, Guillem de Castro-94, E-46001 València, Spain.

Institut Universitari de Ciència Molecular, Universitat de València, P.O. Box 22085, E-46071 València, Spain.

出版信息

Phytochemistry. 2015 Aug;116:305-313. doi: 10.1016/j.phytochem.2015.05.008. Epub 2015 May 26.

Abstract

A set of 71 triterpenoid and steroid compounds from Ganoderma were periodically classified using a procedure based on information entropy with artificial intelligence. Six features were used in hierarchical order to classify the triterpenoids and steroids structurally. The phytochemicals belonging to the same group in the periodic table present similar antioxidant activity, and those compounds belonging to the same period exhibit maximum resemblance. The periodic classification is related to the experimental bioactivity and antioxidant potency data that are available in the literature: a steroid with a three-ketone group conjugated with two carbon-carbon double bonds in the right side of the periodic table exhibits the greatest antioxidant activity.

摘要

利用基于信息熵和人工智能的程序,对从灵芝中提取的71种三萜类和甾体类化合物进行了周期性分类。按照层次顺序使用六个特征对三萜类和甾体类化合物进行结构分类。元素周期表中属于同一组的植物化学物质具有相似的抗氧化活性,属于同一周期的那些化合物表现出最大的相似性。这种周期性分类与文献中可用的实验生物活性和抗氧化能力数据有关:在元素周期表右侧具有三个酮基并与两个碳-碳双键共轭的甾体表现出最大的抗氧化活性。

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