An integrated computational workflow was applied to identify potential inhibitors of dengue virus RNA-dependent RNA polymerase (RdRp) after screening of approximately 9,900 bioactive compounds through structure-based virtual screening and molecular docking. Promising hits were further evaluated using density functional theory (DFT), molecular dynamics (MD) simulations, MM/GBSA free energy calculations, principal component analysis (PCA), free energy landscape (FEL) analysis, machine learning – based QSAR, and ADMET profiling. Redocking highlighted F2924-0102, F3299-0084, and F1411-0380 as top candidates, with docking scores of −12.6, −9.7, and −9.1 kcal/mol, respectively, comparable to the reference inhibitor 68 T (−8.6 kcal/mol). Replicated 500 ns MD simulations demonstrated stable conformations with consistent RMSD, RMSF, radius of gyration (RoG), and solvent-accessible surface area (SASA), indicating system stability and convergence. MM/GBSA analysis revealed favourable binding free energies, particularly for F1411-0380 (−64.02 ± 4.94 kcal/mol) and F2924-0102 (−57.73 ± 4.36 kcal/mol), primarily driven by van der Waals and hydrophobic interactions. Energy decomposition confirmed stable binding to catalytic residues. PCA and FEL analyses identified F2924-0102 as the most stable complex. QSAR predicted pIC50 values between 7.110 and 7.279, while ADMET results indicated good pharmacokinetic properties, supporting these compounds as promising RdRp inhibitors.
Aljarba et al. (Wed,) studied this question.