Identification of potential inhibitors of dengue virus ns5 methyltransferase and polymerase domains through virtual screening and molecular dynamics studies

Computational discovery of DENV-3 RdRp allosteric inhibitors: biological evaluation and mechanistic studies.

Precision repurposing of clinically characterized molecules targeting dengue virus RNA-dependent RNA polymerase: an in silico identification and in vitro validation.

Structure-Based identification of potent dengue virus RNA-dependent RNA polymerase inhibitors using integrated computational workflow

ABSTRACT 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.

Computational Analysis of Peptide Binding on Dengue-Associated Host Proteins POLR2A and Elongin C: Network Pharmacology, Molecular Docking, and Dynamics Simulation Insights

MOLECULAR MODELING AND VIRTUAL SCREENING FOR COMPUTER-AIDED DESIGN OF HCV NS5B POLYMERASE INHIBITORS

Aims and objectives: This study aims to design new inhibitors for the Hepatitis C Virus (HCV) Non-Structural Protein 5B (NS5B) polymerase, an enzyme essential for viral replication. The research addresses an urgent public health issue affecting 71 million people and causing approximately 242,000 deaths annually. Methodology: The research follows a computer-aided rational design approach. A Quantitative Structure-Activity Relationship (QSAR) model was developed using 24 quinazolinone derivatives (QDs) to correlate Gibbs free energy with experimental inhibition constants. The bound conformations of the ligands were used to construct a 3D-QSAR pharmacophore (PH4) model. A virtual library of 168,750 QDs was generated and filtered using ADME (Absorption, Distribution, Metabolism, and Excretion) criteria and PH4 screening. Conformational stability was evaluated through 200-ns molecular dynamics (MD) simulations. Binding free energy variations were quantified using the Molecular Mechanics - Generalized Born Surface Area (MM-GBSA) approach on MD trajectories, calculating molecular mechanics energy, solvation energy, and surface area contributions under the OPLS2005 force field. Results: The QSAR model showed high predictive power and the PH4 model achieved an R2 of 0.85. Screening identified 39 potent analogues. The lead candidate, 3-6-4-45, exhibited a predicted inhibitory concentration of 0.62 nM, approximately 96 times more active than the best reference ligand (60 nM). MD simulations confirmed stability with RMSD values between 1.5 and 3 Å. MM-GBSA binding energies converged with predicted complexation energies, validating the computational reliability. Conclusion: The integration of molecular modeling and in silico screening successfully identified six potent candidate inhibitors of the HCV NS5B polymerase with favorable pharmacokinetic profiles. These analogues represent high-affinity candidates for future therapeutic development.                         Peer Review History: Received 5 April 2026;   Reviewed 13 May 2026; Accepted  10 June; Available online 15 July 2026 Academic Editor: Dr. Iman Muhammad Higazy, National Research Center, Egypt, imane.higazy@hotmail.com Reviewers: Dr. Sarfaraz Ahmed, Global Institute of Pharmaceutical Education and Research, Kashipur, Uttarakhand, India, sarfarazahmed1@gmail.com  Prof. Amani S. Awaad, Prince Sattam Bin Abdulaziz University, Al-Kharj. KSA., amaniawaad@hotmail.com

Discovery of Novel Druggable Binding Pockets on SARS-CoV-2 PLpro via Fragment Screening and Insights for Structure-Based Inhibitor Design.

A fragment-based drug discovery strategy combined with crystallographic screening and structural similarities among the fragments provide a robust structural framework for the rational design of PLpro inhibitors and support the development of novel antiviral therapeutics.