AI-driven transfer learning and classical molecular dynamics for strategic therapeutic repurposing and rational design of antiviral peptides targeting monkeypox virus DNA polymerase.

The emergence of monkeypox virus (MPXV) as a global health threat has necessitated the rapid identification of novel antiviral therapeutics. Currently, no FDA-approved drugs are specifically designed against the disease. We used an in-house deep learning pharmacophore model for screening a library of 1974 FDA-approved drugs targeting the active site of MPXV DNA polymerase. Three drugs exhibited the strongest binding affinities, outperforming the control drug, Cidofovir diphosphate, and forming stable interactions with key active site residues. Among them, Paromomycin emerged as the most favourable drug, demonstrating stable, persistent, and adaptable interactions in molecular dynamics simulation. In parallel, we developed a novel automated peptide-generating AI pipeline that integrates active-site residues with knowledge-guided amino acid selection to generate and evaluate synthetic peptides. Cysteine-Phenylalanine-Cysteine (CFC), together with a panel of candidates, emerged through rational balancing of physicochemical properties and drug-likeness for accelerated therapeutic discovery. Synthetic peptides were evaluated to further understand the binding efficacies with DNA polymerase. CFC peptide demonstrated strong binding affinity (-8.08 kcal/mol) through stable interactions with key catalytic residues ASP549, ARG634 and LYS661, while MMGBSA analysis confirmed favourable binding energy (-33.02 kcal/mol). Consistent results in MD simulations indicate functional binding without destabilisation. Although ADMET predictions for CFC revealed limitations in permeability and oral bioavailability, its favourable binding profile and reduced predicted toxicity support its potential as a novel antiviral lead.