Machine learning-based modeling to predict inhibitors for targets of Alzheimer's Disease (opens in new tab)

Alzheimer's Disease is a chronic neurodegenerative disorder projected to affect 115 million people by 2050, driven by mechanisms like the cholinergic and amyloid hypotheses and insulin signaling disruptions involving key targets such as BACE-1, AChE, and GSK-3 beta. Utilizing machine learning (ML), we developed predictive models for inhibitor screening, achieving AUC-ROC scores above 0.9 for all targets. BACE-1 models showed high accuracy (86.63...