Evaluation quantitative structure-activity relationship (QSAR) using ensemble learning methods on acetylcholinesterase inhibitors for Alzheimer's disease

Abstract

Acetylcholinesterase inhibitors (AChEI) are among the most potential drug molecules for treating Alzheimer's disease and effectively treating its symptoms. Quantitative Structure and Activity Relationship (QSAR) is a computational modeling method to determine the relationship between the structural properties of chemical compounds and biological activities. This study used a classification QSAR model to predict the active and inactive molecules in AChEI. There were 3809 molecules of compounds in the preprocessing stage consisting of 2215 molecules of active compounds and 1594 molecules of inactive compounds. The compound molecules in SMILES were extracted into the fingerprint using the ECFP and FCFP method with diameters of 4 and 6. In this study, the ensemble learning methods used to build the classification QSAR model were voting, averaging, and stacking. The results showed that the ensemble learning method had a better performance than using only one base model. The classification QSAR model with base model obtained an accuracy of 92%, a sensitivity of 89.97%, a specificity of 93%, and an MCC of 83%. The comparison, the ensemble learning method with the stacking technique obtained an accuracy of 93%, a sensitivity of 92%, a specificity of 94%, and an MCC of 86%.