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image of Development of a Robust QSAR Model to Predict the Affinity of N-Bridged Bicyclic Sulfonamide, Pyrazole/Sulfonamide Based Dihydroquinoline and Sulfonamide –Pyrazolopiperidine γ-secretase Inhibitors

Abstract

Introduction

γ-secretase has been a primary target for the creation of therapies that alter the course of Alzheimer's disease. Compounds inhibiting γ-secretase, targeting PS-1, are potential therapeutic agents for Alzheimer’s disease.

Methods

The model was generated with the help of linear and non-linear regression analysis methods. The analysis helped to ascertain the role of log P (whole molecule), no. of H-bond (whole molecule), Kier ChiV3(cluster indices), and Kier Chi6 (ring index) in determining the activity of γ secretase inhibitors. In addition to QSAR modelling, Lipinski’s rule of five was also employed to check the pharmacokinetic profile of γ-secretase inhibitors.

Results

Significant statistical values of the designed models were obtained with the help of MLR, PLS, and FFNN analysis and the relevant descriptors.

Discussion

QSAR (Quantitative Structure-Activity Relationship) models generated (both MLR and PLS) were robust with statistically significant s, F, r, r2 and r2CV values. This study conducts QSAR analysis using linear regression analysis and non-linear regression analysis on a data set of 53 compounds acting as γ-secretase inhibitors.

Conclusion

None of the compounds violated Lipinski’s rule of five, indicating that the γ-secretase inhibitors reported here have sound pharmacokinetic profiles and can be considered as potential drug candidates for Alzheimer’s disease.

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2025-04-30
2025-09-26

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/content/journals/cei/10.2174/0115734080323195250331113024
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Development of a Robust QSAR Model to Predict the Affinity of N-Bridged Bicyclic Sulfonamide, Pyrazole/Sulfonamide Based Dihydroquinoline and Sulfonamide –Pyrazolopiperidine γ-secretase Inhibitors
Bentham Science Publishers ; https://doi.org/10.2174/0115734080323195250331113024
/content/journals/cei/10.2174/0115734080323195250331113024
/content/journals/cei/10.2174/0115734080323195250331113024
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  • Article Type:     Research Article
Keywords: FFNN ; Alzheimer’s disease ; γ-secretase inhibitors ; PLS ; MLR ; QSAR

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