Exploring 3D-QSAR to identify potential small molecule for treating Alzheimer disease: A case study with b-secretase inhibitors

Background/Aim: One of the most common neurological conditions that results in dementia is Alzheimer disease. The current treatment options for Alzheimer disease include acetylcholinesterase (AChE) and -methyl-D-aspartate (NMDA) inhibitors, but there is a significant need for further research. There are numerous molecular targets that can be used to treat Alzheimer disease. Aim of this study was to analyse b-secretase as a target because of its documented involvement in the pathophysiology of the illness. Additionally, prior research investigated the possible therapeutic effects of derivatives based on guanidine. Methods: A total of 146 well-known b-secretase inhibitors were collected from various literature sources. To forecast these compounds' inhibitory potency, models were created using ligand-based drug design (LBDD) and quantitative structure-activity relationship (3D-QSAR) investigations. Six models were generated and based on the statistical parameters q² (cross-validated R²) and standard error of estimate (SEE), the 6th model was selected for further investigation. Results: A cross-validated R2 (R2cv) value of 0.764 was obtained utilising the leave-one-out (LOO) method in the partial least squares (PLS) analysis for atom-based QSAR. With an F ratio of 337.2, a SEE of 0.2306 and an R2 value of 0.9516, the non-cross-validated analysis produced these results. Field-based QSAR had an R2cv value of 0.7353, while the non-cross-validated analysis produced an F ratio of 283.1, an R2 value of 0.9428 and a SEE of 0.2505. Predicting the inhibitory potency of novel compounds against b-secretase was done using the contour map analysis. Atom-based and field-based 3D-QSAR models projected the pIC50 value of the proposed compound P1 to be 8.41 and 8.32, respectively. Conclusion: The findings of this study provide valuable insights into the design of new molecules targeting b-secretase in Alzheimer disease. The predictive models and the newly designed molecules, particularly molecule P1, could serve as potential leads for the development of new chemical entities as anti-Alzheimer agents. These results may significantly contribute to the ongoing efforts to develop more effective treatments for Alzheimer disease.