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

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

The findings suggest that Lig-1, followed by Lig-3, may serve as promising computational lead compounds targeting SARS-CoV-2 MPro, representing promising candidates for further experimental validation.

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.

Discovery of two novel small-molecule series with potent SARS-CoV-2 inhibition via putative nsp13 open-state trapping

We report the discovery of 31 SARS-CoV-2 inhibitors identified across two computational-to-experimental screening campaigns, with average cell-based antiviral potency satisfying 〈EC50〉 ≤ CC50/3 in A549-hACE2 cells. These compounds were selected from 60 successfully tested molecules evaluated using nanoluciferase reporter assay (nLuc), cytopathic effect (CPE), and host-cell cytotoxicity (CC50) assays. We used a funnel-like computational framework where candidate compounds were progressively prioritized through different scores, improving robustness against the limitations of individual metrics. However, none of the docking-based scoring metrics, including MM/GBSA and Glide scores, showed statistically significant correlation with experimental activity, illustrating the limitations of docking-only approaches and supporting a major contribution of the machine-learning predictions to the high hit-identification rate. Although direct target validation is still lacking, the machine-learning (ML) workflow was grounded in experimental nsp13 inhibition labels, and molecular dynamics (MD) simulations suggest that these compounds can act by stabilizing apo-like open conformations of nsp13, with differential interactions involving the 1B domain contributing to potency differences. Among the compounds evaluated, the phenoxypropanol (PP) and bipiperidine (BPP) series stood out as the most promising SARS-CoV-2 antivirals. Integrated analysis of structure–activity relationships, physicochemical parameters, metabolic stability, and interdomain dynamics delineated structural optimization paths for both series.

Discovery of piperazine-based multi-site occupying coronavirus Mpro inhibitors with potent and broad-spectrum antiviral activity.

The continuous evolution of SARS-CoV-2 and the emergence of drug-resistant variants underscore the urgent need for broad-spectrum antiviral agents targeting conserved viral proteins. The main protease (Mpro) represents a promising target due to its essential role in coronavirus replication. In this study, we report the discovery and optimization of a novel series of piperazine-based Mpro inhibitors using a multi-site binding strategy guided by analysis of conserved residues within the coronavirus Mpro active sites. Starting from the noncovalent lead GC-14, systematic optimization of substituents occupying the S1', S1, S2, and S4 subsites of Mpro led to the development of the noncovalent inhibitor GY-e2, which showed improved inhibitory efficacy against both SARS-CoV-2 and SARS-CoV Mpro. To further enhance its antiviral efficacy in cellular models, reactive warheads targeting C145 were incorporated into the scaffold to generate covalent inhibitors. This strategy yielded the isomeric compounds Y-U0-R and Y-U0-S, which displayed potent Mpro inhibition and markedly enhanced antiviral activity in SARS-CoV-2-infected Calu-3 cells. Moreover, both compounds exhibited broad-spectrum antiviral activity against other human coronaviruses, and notably remained effective against the two major Nirmatrelvir-resistant strains evaluated in this study. Mechanistic studies further confirmed kinetically stable binding and time-dependent inhibition of Y-U0-R, supporting the rationale of covalent inhibitor design. These findings highlight the utility of structure-based design for the development of promising broad-spectrum anti-coronavirus agents.

Structure-Based Drug Repurposing of Some Broad-Spectrum Antiviral Drugs Against the ZIKV NS2B-NS3 Protease

Fragment-based discovery of novel Baicalein derivatives as triple-target 3CLpro/PLpro/RdRp inhibitors against SARS-CoV-2.

The rapid evolution of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and the emergence of drug resistance necessitate the development of multi-target antiviral therapeutics. This study aimed to computationally design a novel triple-target inhibitor derived from the natural flavonoid Baicalein, targeting the essential SARS-CoV-2 enzymes 3CLpro, PLpro, and RdRp to combat viral resistance. An integrated computational approach was employed, including virtual screening of compound libraries, molecular docking using Molecular Operating Environment software, fragment-based drug design to optimize Baicalein, and molecular dynamics simulations over 200 ns with AMBER 18 to assess binding stability. Binding free energies were calculated via MM/GBSA methods, and pharmacokinetic properties were evaluated using ADME/T predictions. Retrosynthetic analysis was performed to confirm synthetic feasibility. The novel compound BD02 demonstrated stable binding to all three targets, with significantly improved binding free energies compared to Baicalein: 3CLpro (-50.68 kcal/mol), PLpro (-59.05 kcal/mol), and RdRp (-51.98 kcal/mol). Molecular dynamics simulations showed low root mean square deviation values, indicating high structural stability. ADME/T predictions revealed favorable drug-like properties, including good absorption and low toxicity risks. BD02 is a computationally promising synthetic lead scaffold for broad-spectrum anti-coronavirus design. This multi-target design theoretically helps mitigate viral resistance and provides structural design references.