Integrating Molecular Dynamics Simulation-Driven Computational Validation and <i>In Vitro</i> Biological Assessment of Pteridin-7(8 <i>H</i> )-one-Thiadiazole Hybrids Derivatives as Effective SARS-CoV-2 Main Protease Inhibitors
SARS-CoV-2 has caused a global health crisis, and the Main Protease (M pro ) is a proven pharmaceutical target. The current study presents an approach to combating disease by synthesizing a series of Pteridin-7(8H)-one–Based Thiadiazole Hybrids as potential SARS-CoV-2 M pro inhibitors. The compounds prepared were structurally confirmed using 1 H and 13 C NMR spectroscopy and evaluated for enzyme inhibition. The evaluation revealed a broad spectrum of inhibitory potency, with IC 50 values ranging from 0.037 μM to 63.83 μM. Remarkably, compounds 11h (IC 50 = 0.037 μM), 11c (IC 50 = 0.048 μM), and 11m (IC 50 = 0.078 μM) demonstrated superior or comparable inhibitory potency relative to that of the reference inhibitor Nirmatrelvir (IC 50 = 0.059 μM). Molecular docking investigations demonstrated that these Pteridin-7(8H)-one–Based Thiadiazole Hybrids effectively filled the M pro active site sub-pocket via stable hydrogen bonding and hydrophobic contacts, allowing for catalytic dyad engagement (CIS145/HIS41) and pocket flexibility. Additionally, DFT calculations and ADMET analyses of the most potent compounds, together with a representative comparative analogue (11j), revealed favorable electronic behavior and suitable pharmacokinetic properties, supporting their potential for further optimization. Overall, these findings identify Pteridin-7(8H)-one–Based Thiadiazole Hybrids as potential SARS-CoV-2 M pro inhibitors.
Authors
- Mahdi H. Alsugoor (ORCID: https://orcid.org/0000-0003-2106-8541)
- Ayaz Ali Khan (ORCID: https://orcid.org/0000-0002-9274-2620)
- Umair Khurshid (ORCID: https://orcid.org/0000-0002-4444-942X)
- Maha Abdullah Alwaili
- Maham Nadeem
- Nawal Al-Hoshani
- Hina Sarfraz
- Sonia Bashir
- Muhammad Sajid Mahmood
- Rafaqat Hussain
Publication Details
- Journal
- Journal of Computational Biophysics and Chemistry
- Published
- 2026-06-12
- DOI
- https://doi.org/10.1142/s2737416526500961
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00