COMPUTATIONAL DESIGN AND MOLECULAR DOCKING OF PYRAZOLONE DERIVATIVES DERIVED FROM IMIDAZOLE HYDRAZIDE AGAINST SARS-COV-2 MAIN PROTEASE

The emergence of SARS-CoV-2 has highlighted the urgent need for effective antiviral agents, prompting the exploration of novel heterocyclic scaffolds through computational drug design approaches. In the present study, a series of fifty novel pyrazolone derivatives derived from imidazole hydrazide were designed and evaluated as potential inhibitors of the SARS-CoV-2 main protease (Mpro, PDB ID: 6LU7). The chemical structures of the compounds were drawn and optimized using ACD/ChemSketch software. The designed compounds were further assessed for drug-likeness through Lipinski’s rule of five using Molinspiration software, where all derivatives showed acceptable physicochemical properties. Biological activity prediction was carried out using PASS software, indicating favorable probabilities for antiviral activity. ADMET profiling further supported the suitability of the compounds for oral administration. Molecular docking studies were performed using AutoDock against SARS-CoV-2 Mpro, and several derivatives exhibited binding affinities comparable to or better than the standard drug ritonavir. Among them, compound A49 demonstrated the most promising docking score, suggesting strong interaction with the active site of the target enzyme. Overall, the study identifies pyrazolone–imidazole hydrazide derivatives as promising lead candidates for further synthesis and experimental validation as potential anti-COVID-19 agents.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-16
DOI
https://doi.org/10.5281/zenodo.19591862
Primary Topic
Computational Drug Discovery Methods
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article
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article

COMPUTATIONAL DESIGN AND MOLECULAR DOCKING OF PYRAZOLONE DERIVATIVES DERIVED FROM IMIDAZOLE HYDRAZIDE AGAINST SARS-COV-2 MAIN PROTEASE

Anjana V. S.*, Rose Mary Joseph, Ajna S. A., Anchu Raj, Chandu Nath
Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

COMPUTATIONAL DESIGN AND MOLECULAR DOCKING OF PYRAZOLONE DERIVATIVES DERIVED FROM IMIDAZOLE HYDRAZIDE AGAINST SARS-COV-2 MAIN PROTEASE

Anjana V. S.*, Rose Mary Joseph, Ajna S. A., Anchu Raj, Chandu Nath
article en

Abstract

The emergence of SARS-CoV-2 has highlighted the urgent need for effective antiviral agents, prompting the exploration of novel heterocyclic scaffolds through computational drug design approaches. In the present study, a series of fifty novel pyrazolone derivatives derived from imidazole hydrazide were designed and evaluated as potential inhibitors of the SARS-CoV-2 main protease (Mpro, PDB ID: 6LU7). The chemical structures of the compounds were drawn and optimized using ACD/ChemSketch software. The designed compounds were further assessed for drug-likeness through Lipinski’s rule of five using Molinspiration software, where all derivatives showed acceptable physicochemical properties. Biological activity prediction was carried out using PASS software, indicating favorable probabilities for antiviral activity. ADMET profiling further supported the suitability of the compounds for oral administration. Molecular docking studies were performed using AutoDock against SARS-CoV-2 Mpro, and several derivatives exhibited binding affinities comparable to or better than the standard drug ritonavir. Among them, compound A49 demonstrated the most promising docking score, suggesting strong interaction with the active site of the target enzyme. Overall, the study identifies pyrazolone–imidazole hydrazide derivatives as promising lead candidates for further synthesis and experimental validation as potential anti-COVID-19 agents.

Zenodo (CERN European Organization for Nuclear Research)
Good health and well-being
Openalex Percentile: Top 5%
Computational Drug Discovery Methods
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COMPUTATIONAL DESIGN AND MOLECULAR DOCKING OF PYRAZOLONE DERIVATIVES DERIVED FROM IMIDAZOLE HYDRAZIDE AGAINST SARS-COV-2 MAIN PROTEASE — Anjana V. S.*, Rose Mary Joseph, Ajna S. A., Anchu Raj, Chandu Nath · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS