Self-Organizing Maps for the Classification of Gallic Acylate Polyphenols as HSV-1 Inhibitors

Xianxiu      Qiu; Meigong      Zhong; Yangfei      Xiang; Chang      Qu; Ying      Pei; Yingjun      Zhang; Chongren      Yang; Johann      Gasteiger; Jun      Xu; Zhong      Liu; Yifei      Wang

Abstract

Herpes simplex virus type 1 (HSV-1), a member of the Herpesviridae family, is a ubiquitous, contagious, hostadapted pathogen that causes a wide variety of disease states, such as herpes labialis (“cold sores”) and encephalitis. Recently, due to the appearance of acyclovir-resistant HSV-1 mutants, a rapidly growing area of research has been the identification of novel small molecules (whether found in traditional medicine or not) with antiviral activity. One group of these novel pre-drugs is gallic acylate polyphenols. Here, detailed insight into the influence of the chemical structure on anti- HSV-1 activity of gallic acylate polyphenols has been provided based on an exploration of structure-function relationships through self-organizing maps and counterpropagation neural networks. A number of descriptors were investigated to construct optimized models. The resulting model exhibits a correct prediction rate of 90.67%, with active molecule classification accuracy higher than 95.00%, demonstrating that the electrostatic effect and distance between atoms are related to HSV-1 inhibition for these gallic acylate polyphenols. The results provide insights into the influence of the chemical structure on anti-HSV-1 activity of gallic acylate polyphenols.

Keywords: Artificial neural network, counterpropagation neural networks, gallic acylate polyphenol, herpes simplex viruses, self-organizing maps, structure-activity relationship.

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