Machine Learning-driven Discovery of EBNA1 Inhibitors Against Epstein Barr Virus in Nasopharyngeal Carcinoma

This study addresses nasopharyngeal carcinoma (NPC), particularly common among Malaysians, by targeting Epstein-Barr virus (EBV) infection through the EBNA1 protein, which is crucial for EBV survival and NPC tumourigenicity. Using quantitative structure-activity relationship (QSAR) models in WEKA based on the AID2381 dataset, we identified ten potential EBNA1 inhibitors from the Enamine compound library. One compound, Z4133355762, selectively inhibited EBV-positive NPC cells (C666-1). Molecular dynamics simulations validated the interaction, demonstrating QSAR's effectiveness in streamlining drug discovery for NPC treatments.