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<p>In this study, we performed a virtual screening of 1,200 novel compounds to identify potential inhibitors of SARS-CoV-2 Mpro using various computational methods, including 3D-QSAR models, molecular docking, molecular dynamics simulation, ADMET analysis, and quantum mechanical studies. We developed 3D-QSAR models based on 36 Ebselen derivatives, employing partial least-squares (PLS) analysis to understand how structural properties influence inhibitory potency and to propose new bioactive molecules. Our Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) models demonstrated strong correlations between predicted and experimental activities, with CoMFA showing Q<sup>2</sup> = 0.74 and R<sup>2</sup> = 0.97, and CoMSIA showing Q<sup>2</sup> = 0.71 and R<sup>2</sup> = 0.93. Molecular docking studies confirmed the inhibitory potential of the compounds, revealing a significant correlation between experimental pIC<sub>50</sub> values and docking scores, reinforcing our predictions. By integrating predicted pIC<sub>50</sub> values with docking studies, we identified twelve compounds that were common among the top one hundred selected by the CoMFA model, the CoMSIA model, and molecular docking. These compounds can be selected with greater confidence as more potent inhibitors. The results were promising, as these compounds exhibited favorable binding affinities, indicating strong interactions with the target protein, underscoring the value of combining computational approaches like CoMFA and CoMSIA with docking studies. The RMSD and radius of gyration (Rg) demonstrated that the top four complexes remain stable throughout 500 ns of MD simulation. Additionally, the Atoms in Molecules analysis examined the interactions of key residues involved in binding to the inhibitors for the four top compounds by assessing the electron densities (ρ) calculated at the bond critical points (BCPs) between the residues and the compounds. The sum of the ρ values calculated at the BCPs between all residues and the inhibitor correlates well with the interaction energies obtained using other methods. The ADMET evaluation revealed that the two compounds possess essential pharmacokinetic features, positioning them as promising candidates for SARS-CoV-2 inhibition.</p> Graphical abstract <p></p>

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Structural-based virtual screening of Ebselen derivatives to identify potential SARS-CoV-2 main protease inhibitors, utilizing 3D-QSAR models, molecular docking, MD simulation, and quantum mechanical calculations

  • 期刊论文

  • 发表日期：

    2025年5月19日

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  • Haniyeh Baluch,
  • Ali Ebrahimi,
  • Najmeh Mostafavi

摘要

In this study, we performed a virtual screening of 1,200 novel compounds to identify potential inhibitors of SARS-CoV-2 Mpro using various computational methods, including 3D-QSAR models, molecular docking, molecular dynamics simulation, ADMET analysis, and quantum mechanical studies. We developed 3D-QSAR models based on 36 Ebselen derivatives, employing partial least-squares (PLS) analysis to understand how structural properties influence inhibitory potency and to propose new bioactive molecules. Our Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) models demonstrated strong correlations between predicted and experimental activities, with CoMFA showing Q2 = 0.74 and R2 = 0.97, and CoMSIA showing Q2 = 0.71 and R2 = 0.93. Molecular docking studies confirmed the inhibitory potential of the compounds, revealing a significant correlation between experimental pIC50 values and docking scores, reinforcing our predictions. By integrating predicted pIC50 values with docking studies, we identified twelve compounds that were common among the top one hundred selected by the CoMFA model, the CoMSIA model, and molecular docking. These compounds can be selected with greater confidence as more potent inhibitors. The results were promising, as these compounds exhibited favorable binding affinities, indicating strong interactions with the target protein, underscoring the value of combining computational approaches like CoMFA and CoMSIA with docking studies. The RMSD and radius of gyration (Rg) demonstrated that the top four complexes remain stable throughout 500 ns of MD simulation. Additionally, the Atoms in Molecules analysis examined the interactions of key residues involved in binding to the inhibitors for the four top compounds by assessing the electron densities (ρ) calculated at the bond critical points (BCPs) between the residues and the compounds. The sum of the ρ values calculated at the BCPs between all residues and the inhibitor correlates well with the interaction energies obtained using other methods. The ADMET evaluation revealed that the two compounds possess essential pharmacokinetic features, positioning them as promising candidates for SARS-CoV-2 inhibition.

Graphical abstract