In-silico analysis and design of quinoline-based hybrids with oxadiazole and triazole frameworks as anti-diabetic agents

In this study, a quantitative structure-activity relationship (QSAR) approach was employed to predict the inhibitory activities of quinoline-based hybrids as potential inhibitors of α-glucosidase, an enzyme crucial in carbohydrate metabolism. The predictive models were generated using genetic function approximation (GFA) and among them, model 2 emerges as the most effective boasting validation metrics of correlation coefficient of the training set (R 2 trn ) = 0.909, adjusted correlation coefficient (R 2 adj ) = 0.889, cross-validation coefficient (Q 2 cv ) = 0.835 and correlation coefficient of test set (R 2 test ) = 0.745. Statistical evaluations, encompassing Mean effect, p-values, Y-scrambling and applicability domain analysis, confirmed the impact of the descriptors, showed statistical significance and reliability of the selected model. Subsequently, four potent compounds were designed, and their inhibitory activities were predicted. Remarkably, the designed compounds exhibited superior inhibitory potential compared to the template compound used as a reference. Furthermore, detailed analysis of the binding interactions of the designed compounds within the active site of the target receptor pdb id: (3TOP), revealed a strong binding affinity surpassing that of acarbose, a known α-glucosidase inhibitor. The dynamic simulations of complex with higher scores demonstrated remarkable stability through various analyses, indicating their potential as promising candidates for diabetes treatment. The (ADME) properties of the designed compounds were also investigated, indicating their pharmacological effectiveness and potential as drug candidates. Additionally, Density Functional Theory (DFT) studies highlighted compound 2 as being particularly reactive and possessing a propensity for electron transfer or donation. This research offers valuable insights for designing future α-glucosidase inhibitors with increased potency.

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Publication Details

Journal
Scientific African
Published
2025-03-04
DOI
https://doi.org/10.1016/j.sciaf.2025.e02631
Citations
1
Primary Topic
Click Chemistry and Applications
Type
article
Field-Weighted Citation Impact
0.94
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article

In-silico analysis and design of quinoline-based hybrids with oxadiazole and triazole frameworks as anti-diabetic agents

Vipin Kumar Mishra, Gideon Adamu Shallangwa, Stephen Eyije Abechi, Khalifa Sunusi Aminu et al.
1 citations
Scientific African
Click Chemistry and Applications
0.94
article

In-silico analysis and design of quinoline-based hybrids with oxadiazole and triazole frameworks as anti-diabetic agents

Vipin Kumar Mishra, Gideon Adamu Shallangwa, Stephen Eyije Abechi, Khalifa Sunusi Aminu, Adamu Uzairu, Abdullahi Bello Umar, Saumya Verama
article en
1 citations

Abstract

In this study, a quantitative structure-activity relationship (QSAR) approach was employed to predict the inhibitory activities of quinoline-based hybrids as potential inhibitors of α-glucosidase, an enzyme crucial in carbohydrate metabolism. The predictive models were generated using genetic function approximation (GFA) and among them, model 2 emerges as the most effective boasting validation metrics of correlation coefficient of the training set (R 2 trn ) = 0.909, adjusted correlation coefficient (R 2 adj ) = 0.889, cross-validation coefficient (Q 2 cv ) = 0.835 and correlation coefficient of test set (R 2 test ) = 0.745. Statistical evaluations, encompassing Mean effect, p-values, Y-scrambling and applicability domain analysis, confirmed the impact of the descriptors, showed statistical significance and reliability of the selected model. Subsequently, four potent compounds were designed, and their inhibitory activities were predicted. Remarkably, the designed compounds exhibited superior inhibitory potential compared to the template compound used as a reference. Furthermore, detailed analysis of the binding interactions of the designed compounds within the active site of the target receptor pdb id: (3TOP), revealed a strong binding affinity surpassing that of acarbose, a known α-glucosidase inhibitor. The dynamic simulations of complex with higher scores demonstrated remarkable stability through various analyses, indicating their potential as promising candidates for diabetes treatment. The (ADME) properties of the designed compounds were also investigated, indicating their pharmacological effectiveness and potential as drug candidates. Additionally, Density Functional Theory (DFT) studies highlighted compound 2 as being particularly reactive and possessing a propensity for electron transfer or donation. This research offers valuable insights for designing future α-glucosidase inhibitors with increased potency.

Scientific AfricanVol. 28
Ahmadu Bello University (NG), Bayero University Kano (NG)
Openalex Percentile: Top 28%
Click Chemistry and Applications
0.94
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