<![CDATA[Alzheimer’s disease is a progressive neurodegenerative disorder characterized by cognitive and memory decline, with acetylcholinesterase (AChE) as one of the most important therapeutic targets. Conventional experimental screening of AChE inhibitors is time-consuming, costly, and prone to high failure rates. Therefore, computational approaches based on machine learning are increasingly adopted to accelerate early-stage drug discovery. This study aims to classify the bioactivity of chemical compounds against AChE as potential Alzheimer’s drug candidates using the Extreme Gradient Boosting (XGBoost) algorithm. Bioactivity data were obtained from the ChEMBL database, where IC50 values were converted into pIC50 and classified into active and inactive compounds. Molecular descriptors were calculated using the Mordred library, and the dataset was divided into training and testing sets with an 80:20 ratio. Hyperparameter optimization was performed using Random Search to improve model performance. The experimental results show that the baseline XGBoost model achieved an accuracy of 84.39%, while the optimized model improved accuracy to 86.90% with an AUC of 0.9343. SHAP analysis revealed that descriptors related to electronic properties and lipophilicity, such as SssCH2, PEOE_VSA7, and SlogP_VSA, contributed most significantly to compound activity classification. These findings demonstrate that XGBoost combined with explainable AI techniques is effective for in silico identification of potential Alzheimer’s drug candidates and provides meaningful insights into relevant molecular features]]>
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