Identification of Potential SARS-CoV-2 Main Protease (MPro) Inhibitors Through Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Simulation Approaches

Identifying and Evaluating Flavonoids as Potential Inhibitors of SARS-CoV-2 Main Protease (Mpro/3CL) Through Docking and Molecular Dynamics

Although the acute phase of the SARS-CoV-2 pandemic has subsided, the continued emergence of viral variants underscores the need for structurally diverse antiviral inhibitors. In this study, molecular docking followed by molecular dynamics (300 ns) simulations and binding free energy calculations using the Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) method were employed to evaluate substituted flavonoids derived from Taraxacum officinale and Urtica dioica as potential inhibitors of the SARS-CoV-2 main protease (Mpro/3CLpro). Docking analysis identified several derivatives with favorable binding scores; however, dynamic refinement revealed differential stability among the ligand–protein complexes. Among the evaluated compounds, the luteolin derivative LND-17 showed the most consistent performance, exhibiting binding free energy estimates approaching those obtained for the reference inhibitors nirmatrelvir and ensitrelvir, sustained catalytic pocket occupancy, and energetic contributions involving the catalytic dyad (His41 and Cys145). Additional derivatives, including LNG-04, QND-07, and QNG-20, showed moderate stabilization but lower overall consistency. These findings highlight glycosylated flavonoids as promising scaffolds for future structure-based optimization and provide structural insights to guide experimental validation.

Designing triazine-based nucleoside analogues as promising SARS-CoV-2 main protease inhibitors: a molecular modeling study

Structurally optimized triazine-based nucleoside analogues are identified as potential SARS-CoV-2 Mpro inhibitors and the utility of an integrated computational strategy for the rational design and prioritization of antiviral candidates for further experimental validation is demonstrated.

Computational identification of novel antiviral leads against SARS-CoV-2 Mpro through systematic virtual screening and molecular dynamics study

Despite advances in vaccines and supportive therapies, a targeted treatment for COVID-19 remains lacking, prompting researchers to explore specific pharmacological interventions to fill this gap. In this quest, we targeted the main protease (Mpro) of SARS-CoV-2, a well-established druggable target crucial for viral replication and transcription. 714 antiviral compounds were computationally screened against the active-site residues of Mpro, and 17 hits showed significantly better interactions with Mpro than the selected control (N3). The two top-scoring compounds, LY2784544 and Pimobendan, were further evaluated through 100-ns molecular dynamics (MD) simulations. Comprehensive post-simulation analyses, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration, solvent-accessible surface area, principal component analysis, free energy landscape (FEL) mapping, and MM/PBSA binding free energy calculations, demonstrated stable complex formation and persistent engagement of the catalytic dyad (His41–Cys145) and S1/S2 subsites. Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) results indicated favorable binding free energies, with Pimobendan showing comparatively stronger energetic stabilization. The study identifies LY2784544 and Pimobendan as potential antiviral agents for Mpro. Given their stability, strong binding energy (BE) profiles, and existing clinical data, these compounds warrant additional experimental validation to determine their potential efficacy against SARS-CoV-2.

Computational discovery of novel human rhinovirus 3 C protease inhibitors: molecular docking, dynamic simulations, free energy landscape, MMPBSA and ADMET analysis.

In silico Molecular Docking, Dynamics, and ADMET Analyses of Leritrelvir Targeting SARS-CoV-2 Main Protease

The current COVID19 pandemic necessitates the development of antiviral agents targeting the SARS-CoV-2 main protease. It is one of the viral enzymes that plays a key role in reproduction. Rayitrelvir (RAY1216), a 2-ketoamide-based peptidomimetic inhibitor, is presently in clinical development. To determine the nature and dynamics of Leritrelvir (RAY1216) binding to Mpro (PDB ID: 6LU7), structure-based molecular docking and dynamics simulation were used in this study to clarify the nature and dynamic stability of Leritrelvir (RAY1216). AutoDock was used to dock, and further validation was done on the DockThor platform. The interactive analysis showed that Leritrelvir (RAY1216) was highly stably bound in the catalytic pocket (7.3 kcal/mol), which was hydrogen bonded with GLN110, HIS246, GLU240, GLN110, and PRO108 and interacted π-π with HIS246 and PHE294. Docking comparative with Nirmatrelvir, the active compound of Paxlovid, showed a similar binding affinity (ΔG = -7.16 kcal/mol) and partially overlapping binding residues, which confirmed the reliability of docking results. Conformational stability of the LeritrelvirMpro complex was confirmed by a 100ns MD that showed stable RMSD, radius of gyration, and maintained hydrogen bonding, whereas the RMSF variations were localized to flexible terminal areas. ADMET predictions (SwissADME and ProTox-II) in silico revealed good pharmacokinetics and oral bioavailability, and good toxicity. When combined, these results provide docking, dynamics, and pharmacokinetics, making Leritrelvir (RAY1216) an effective noncovalent SARS-CoV2 Mpro inhibitor. However, additional in vitro and in vivo studies are justified to establish its potential in clinical use as a next-generation therapeutic to treat COVID19.

A combination of MD-derived pharmacophores and structure-based strategy identifies dual allosteric inhibitors of NS2B-NS3 proteases in Zika and West Nile viruses.

Starting from the crystal structure of a Zika virus (ZIKV) NS2B-NS3 protease with bound allosteric inhibitor, we generated dynamic pharmacophore models derived from representative molecular dynamics (MD) conformations to capture key interaction patterns. These models guided a pharmacophore-based virtual screening of commercial libraries, including commercial compounds and approved drugs, enabling the rapid selection of virtual hit compounds. Biochemical evaluation identified several inhibitors, with the antiretroviral drug Nelfinavir emerging as the most promising compound. Nelfinavir displayed the strongest inhibition of ZIKV and West Nile virus (WNV) proteases and showed antiviral activity in cell-based assays, with EC50 values of 1.79 ± 0.01 μM on ZIKV and 3.01 ± 1.25 μM in WNV, supporting its relevance as a repositionable scaffold for modulation of ZIKV and WNV infections. The activity observed across flavivirus proteases highlights the value of targeting conserved allosteric regions. Overall, this integrated computer-aided drug design (CADD) strategy demonstrates the power of dynamic, structure-based pharmacophores to uncover novel allosteric inhibitors and accelerate antiviral drug repurposing.