Synthesis, enzyme inhibition, in silico molecular docking and DFT analysis of novel multi-functionalized thiourea and thiazolidinone benzenesulfonamides


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Sakarya M. T., YAMALI C., ERTÜRK A., SERT Y., GÜL H. İ., GÜL M., ...Daha Fazla

Chemical Papers, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s11696-026-04771-z
  • Dergi Adı: Chemical Papers
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core
  • Anahtar Kelimeler: Acetylcholinesterase, Benzenesulfonamide, Carbonic anhydrase, Molecular docking, Thiazolidinone, Thiourea
  • Atatürk Üniversitesi Adresli: Evet

Özet

Enzymes play important roles in various physiological processes, and dysregulation of enzyme expression is linked to the onset and progression of many diseases, including Alzheimer’s disease and diabetes. Enzyme-targeted therapies are promising in the treatment of enzyme-related diseases, making enzymes both biomarkers and targets for therapeutic investigations. In this study, the synthesis and biological assessment of novel multi-functionalized thiourea and thiazolidinone benzenesulfonamides were reported along with their CAs inhibition, AChE inhibition, α-glucosidase, and α-amylase inhibitions. While compound M1, which had a Ki value of 19.88 ± 1.04 nM (hCA I), was found to be the most potent hCA I inhibitor, compound M9, which had a Ki 8.9890 ± 0.8909 nM was found to be the strongest inhibitor against hCA II. Compound M1 showed remarkable AChE inhibition effects with a Ki value of 0.4740 ± 0.0818 nM in comparison to tacrine. The compounds showed Kis in the range of 4.9395 ± 0.8288 – 46.1207 ± 19.5197 nM against α-glycosidase and IC50 values in the range of 12.044 – 56.533 nM against α-amylase. In addition, molecular docking studies were performed to examine and evaluate the interaction of the lead compounds against the enzymes studied. The binding energy values and predicted inhibition constants (Ki) support the effectiveness of these molecules, aligning closely with experimental findings. These results highlight the robustness of the computational approach in identifying potential inhibitors and provide critical insights into the structure-activity relationships, aiding future drug development and therapeutic research.