BACE -1(β-enzyme) is the main therapeutic target for the treatment of Alzheimer's disease, which actively participates in the processing of amyloid precursor protein, resulting in the creation of amyloid-β in the brain. The current work aims to investigate and build a QSAR of BACE-1 inhibitors. Genetic algorithm-based multiple linear regression (GA-MLR) was used to create regression models between the descriptor and pIC50 value of each molecule in the training set based on selected significant molecular descriptors. The most important descriptors chosen are Burden modified eigenvalue descriptors, PaDEL-weighted path descriptors, autocorrelation descriptors, topological distance matrix descriptors, MLFER descriptors, Barysz matrix descriptors, and chi path cluster descriptors, and validated using both internal and external validation parameters. It also determines the chemical space that the model may predict by defining an applicability domain. The information presented here suggests a good predictive model for BACE-1 inhibitors that can predict the IC50 value of the newly designed chemical compound.