Anti-EBV: Artificial intelligence driven predictive modeling for repurposing drugs as potential antivirals against Epstein-Barr virus.

Epstein-Barr virus (EBV) is linked to various cancers like gastric carcinoma, nasopharyngeal carcinoma, and Burkitt's lymphoma, leading to around 200,000 deaths annually. Despite efforts, FDA-approved drugs to combat EBV infection are lacking. In this endeavor, we have developed an AI/ML based predictive algorithm "Anti-EBV" to find potential antivirals against EBV. We utilized small molecules from the ChEMBL database, which were experimentally tested for antiviral activity against EBV in lytic phase, in terms of IC50 /EC50 values. 17,968 molecular fingerprints and descriptors were computed for each molecule. Further, the best-performing 150 descriptors were used in the predictive model development. The molecules were then split into training/testing (T315) and independent validation (V35) datasets, followed by 10-fold cross validation to develop robust models. Various machine-learning techniques (MLTs) namely SVM, KNN, ANN, DNN, RF and XGBoost were used for predictive models development. SVM model achieved the best performance with Pearson's correlation coefficient (PCC) of 0.91 on T315 dataset and 0.95 on V35 dataset, respectively. These models were found to be robust by applicability domain, decoy dataset and chemical clustering analyses. The top-performing model was used to screen approved drugs from DrugBank, identifying potential repurposed drugs namely arzoxifene, succimer, abemaciclib and many more. To further validate these findings, top compounds were docked against key lytic proteins BZLF1 and BHRF1, demonstrating strong binding affinities for compounds like fluspirilene and suvorexant. This model is accessible as the "Anti-EBV" web server http://bioinfo.imtech.res.in/manojk/antiebv/ for antiviral prediction, making it the first AI/ML-based study for antiviral identification against EBV in lytic phase.