لقمان علايي

Alzheimer’s disease, as the most common type of dementia, is one of the major public health challenges worldwide, and no definitive treatment has yet been introduced. One of the well-established strategies for controlling its symptoms is the inhibition of acetylcholinesterase (AChE). In this study, quantitative structure–activity relationship (QSAR) modeling was applied to investigate the correlation between structural features of carbamate derivatives and their IC50 values, with the aim of predicting the inhibitory efficiency of new compounds. For this purpose, experimental data of 41 carbamate derivatives were collected from reliable sources such as BindingDB and PubChem. The molecular structures were drawn and energy-minimized using HyperChem, and 1,420 molecular descriptors were calculated by Dragon software. The data were organized in Microsoft Excel 2016, normalized, and dimensionally reduced before being transferred to MATLAB (R2021a). Using stepwise regression, 11 key descriptors were identified as the most influential factors affecting inhibitory activity. The final QSAR model was able to predict IC50 values with an accuracy of 92% and a correlation coefficient greater than 95%. The findings of this research demonstrated that QSAR models can serve as powerful tools for predicting the biological activity of AChE inhibitors and guiding the rational design of new anti-Alzheimer drugs. This approach not only reduces laboratory costs and time but also significantly minimizes the reliance on animal testing, thereby accelerating the development of more effective therapeutics.