Self-Organizing Maps for the Classification of Gallic Acylate Polyphenols as HSV-1 Inhibitors
2014; Bentham Science Publishers; Volume: 10; Issue: 4 Linguagem: Inglês
10.2174/15734064113099990038
ISSN1875-6638
AutoresXianxiu Qiu, Meigong Zhong, Yangfei Xiang, Chang Qua, Ying Pei, Ying‐Jun Zhang, Chongren Yang, Johann Gasteiger, Jun Xu, Zhong Liu, Yifei Wang,
Tópico(s)Morinda citrifolia extract uses
ResumoHerpes 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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