Classification of stilbenoid compounds by entropy of artificial intelligence

Abstract

A set of 66 stilbenoid compounds is classified into a system of periodic properties by using a procedure based on artificial intelligence, information entropy theory. Eight characteristics in hierarchical order are used to classify structurally the stilbenoids. The former five features mark the group or column while the latter three are used to indicate the row or period in the table of periodic classification. Those stilbenoids in the same group are suggested to present similar properties. Furthermore, compounds also in the same period will show maximum resemblance. In this report, the stilbenoids in the table are related to experimental data of bioactivity and antioxidant properties available in the technical literature. It should be noted that stilbenoids with glycoxyl groups esterified with benzoic acid derivatives, in the group g11000 in the extreme right of the periodic table, show the greatest antioxidant activity as confirmed by experiments in the bibliography. Moreover, the second group from the right (g10111) contains E-piceatannol, which antioxidant activity is recognized in the literature. The experiments confirm our results of the periodic classification.

Keywords: Antioxidant activity; Information entropy; Molecular classification; Polycyclic compound; Stilbenoid.