Enhancing BACE1 Inhibitor Discovery with Machine Learning by Integrating Binding Interactions and QSAR Features

Machine-learning models integrating binding and QSAR features enhance the prediction of BACE1-ligand binding affinity (pIC50), enabling efficient virtual screening for potent BACE1 inhibitors. These advancements offer potential for accelerating drug discovery for Alzheimer's disease. Future work will expand these models to include broader chemical databases, furthering intervention strategies for AD.