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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