Network pharmacology and molecular docking-based screening of phytochemicals as multitarget inhibitors of Epstein–Barr virus infection and associated malignancies
Year: 2026
Type: article
Abstract: Epstein–Barr virus (EBV) is a class I oncogenic virus implicated in the pathogenesis of multiple lymphoid and epithelial malignancies, including Burkitt lymphoma, Hodgkin lymphoma, nasopharyngeal carcinoma, and gastric carcinoma, contributing to approximately 1.5% of all human cancers worldwide. This study employed an integrated network pharmacology and molecular docking approach to identify plant-derived bioactive compounds against EBV. A library of 1,000 phytochemicals was systematically screened against two essential EBV targets as glycoprotein 42 (Gp42) and Epstein–Barr nuclear antigen 1 (EBNA1). The top 10 compounds against each protein were prioritized based on binding affinity and interaction profiling. Putative gene targets of these compounds were retrieved and intersected with EBV-related cancer-associated genes, yielding 132 common targets. Protein–protein interaction (PPI) network analysis identified ten hub genes centrally involved in viral oncogenesis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed significant involvement in cancer signalling pathways. Hecogenin emerged as the lead computationally prioritized candidate, exhibiting the highest binding affinities against nine of the ten cancer-associated targets, with favourable hydrogen bonding and hydrophobic interactions. ADMET profiling suggested favourable predicted safety-related properties for Hecogenin, including high gastrointestinal absorption, blood–brain barrier permeability, and absence of AMES toxicity. Hecogenin emerges from this computational analysis as a phytochemical with predicted multitarget interactions; however, these findings are preliminary, and further preclinical and experimental validation is required against EBV infection and EBV-associated malignancies. Separately, this study establishes a robust computational framework for the discovery of mechanism-based antiviral agents from natural sources.
Source: Discover Chemistry.
Institution: University of Agriculture Faisalabad
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7221508353