Structure activity relationship-guided scaffold hopping for identification of novel thiadiazole derivatives as potent SARS-CoV-2 Mpro inhibitors

Abstract The ongoing COVID-19 pandemic has presented enormous challenges to worldwide healthcare systems, and effective therapeutic strategies must accompany mass vaccination efforts. In this context, Mpro (the main protease of SARS-CoV-2) is arguably one of the most promising molecular targets for antiviral drug development because it is responsible for processing viral polyproteins and replicating the virus. In this study, a rational design approach was employed to develop a novel series of thiadiazole-based derivatives as potential Mpro inhibitors. A total of compounds ( 5a–f , 9a–d , and 12a–j ) were synthesized through three systematically organized synthetic schemes. Each stage of the design strategy incorporated progressive structural modifications to establish a clear structure activity relationship (SAR) and enhance inhibitory potency. The synthesized compounds were evaluated for their in vitro inhibitory activity against SARS-CoV-2 Mpro. Some derivatives like 5b (IC 50 = 44.63 ± 1.20 µM), 12c (IC 50 = 39.25 ± 1.60 μM), 12a (IC 50 = 41.93 ± 1.90 μM), showed good inhibitory activity as compared to reference drug Nirmatrelvir (IC 50 = 58.4 ± 8.6 μM). Molecular docking analysis supported the experimental data by confirming stable binding of the synthesized compounds within the Mpro active site through key hydrogen-bonding and hydrophobic interactions.

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Journal
Zeitschrift für Naturforschung C
Published
2026-05-21
DOI
https://doi.org/10.1515/znc-2026-0045
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Structure activity relationship-guided scaffold hopping for identification of novel thiadiazole derivatives as potent SARS-CoV-2 Mpro inhibitors

Asma Sardar, Tahir Ali Chohan, Maha Abdullah Alwaili, Sumaira Naz et al.
Zeitschrift für Naturforschung C
Computational Drug Discovery Methods
article

Structure activity relationship-guided scaffold hopping for identification of novel thiadiazole derivatives as potent SARS-CoV-2 Mpro inhibitors

Asma Sardar, Tahir Ali Chohan, Maha Abdullah Alwaili, Sumaira Naz, Hayam Atallah Alwabsi, Khaled Alzhrani, Nawal Al-Hoshani, Rafaqat Hussain, Maher S. Alwethaynani, Hina Sarfraz
article en

Abstract

Abstract The ongoing COVID-19 pandemic has presented enormous challenges to worldwide healthcare systems, and effective therapeutic strategies must accompany mass vaccination efforts. In this context, Mpro (the main protease of SARS-CoV-2) is arguably one of the most promising molecular targets for antiviral drug development because it is responsible for processing viral polyproteins and replicating the virus. In this study, a rational design approach was employed to develop a novel series of thiadiazole-based derivatives as potential Mpro inhibitors. A total of compounds ( 5a–f , 9a–d , and 12a–j ) were synthesized through three systematically organized synthetic schemes. Each stage of the design strategy incorporated progressive structural modifications to establish a clear structure activity relationship (SAR) and enhance inhibitory potency. The synthesized compounds were evaluated for their in vitro inhibitory activity against SARS-CoV-2 Mpro. Some derivatives like 5b (IC 50 = 44.63 ± 1.20 µM), 12c (IC 50 = 39.25 ± 1.60 μM), 12a (IC 50 = 41.93 ± 1.90 μM), showed good inhibitory activity as compared to reference drug Nirmatrelvir (IC 50 = 58.4 ± 8.6 μM). Molecular docking analysis supported the experimental data by confirming stable binding of the synthesized compounds within the Mpro active site through key hydrogen-bonding and hydrophobic interactions.

Zeitschrift für Naturforschung C
Princess Nourah bint Abdulrahman University (SA), Quaid-i-Azam University (PK), Jouf University (SA), Shaqra University (SA), Hazara University (PK), Fatima Jinnah Women University (PK), University of Malakand (PK), University of Tabuk (SA), University of Veterinary and Animal Sciences (PK)
Good health and well-being
Openalex Percentile: Top 5%
Computational Drug Discovery Methods
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