This study explores the use of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 (BACE1) inhibitors for Alzheimer's disease drug discovery. The research aims to enhance classification performance by addressing the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. A dataset of 7298 compounds from the ChEMBL database was used, with molecular descriptors calculated as features. LightGBM was employed in conjunction with carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's performance was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying BACE1 inhibitor activity. The study highlights the critical role of molecular descriptors in understanding drug efficacy and demonstrates LightGBM's potential in streamlining the virtual screening process. The findings advocate for the adoption of LightGBM in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability. The study also contributes to the field by introducing LightGBM as an innovative tool for classifying BACE1 inhibitor activity, implementing hyperparameter tuning, analyzing feature importance, establishing a benchmark, and advancing drug discovery strategies targeting Alzheimer's disease. The results show that LightGBM outperforms other machine-learning models in terms of accuracy, precision, and balanced performance metrics, making it a valuable asset in the drug discovery toolkit. The study underscores the importance of molecular descriptors in classifying drug efficacy and highlights the potential of LightGBM in improving the efficiency and accuracy of drug discovery processes.This study explores the use of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 (BACE1) inhibitors for Alzheimer's disease drug discovery. The research aims to enhance classification performance by addressing the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. A dataset of 7298 compounds from the ChEMBL database was used, with molecular descriptors calculated as features. LightGBM was employed in conjunction with carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's performance was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying BACE1 inhibitor activity. The study highlights the critical role of molecular descriptors in understanding drug efficacy and demonstrates LightGBM's potential in streamlining the virtual screening process. The findings advocate for the adoption of LightGBM in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability. The study also contributes to the field by introducing LightGBM as an innovative tool for classifying BACE1 inhibitor activity, implementing hyperparameter tuning, analyzing feature importance, establishing a benchmark, and advancing drug discovery strategies targeting Alzheimer's disease. The results show that LightGBM outperforms other machine-learning models in terms of accuracy, precision, and balanced performance metrics, making it a valuable asset in the drug discovery toolkit. The study underscores the importance of molecular descriptors in classifying drug efficacy and highlights the potential of LightGBM in improving the efficiency and accuracy of drug discovery processes.