Machine learning-based modeling to predict inhibitors for targets of Alzheimer's Disease

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%) but limited chemical diversity.

AChE models exhibited greater chemical diversity and similar performance (AUC-ROC: 92.86%, Accuracy: 85.20%), while GSK-3 beta models achieved an AUC-ROC of 91.14% with the highest proportion of viable drug candidates.

These findings highlight the potential of ML in Alzheimer's drug discovery, with AChE and GSK-3 beta emerging as promising targets.