Machine Learning-Enhanced Drug Discovery for BACE1: A Novel Approach to Alzheimer's Therapeutics

Abstract

Alzheimers disease (AD) remains a significant global health challenge, with BACE1, a critical therapeutic target due to its role in the amyloidogenic pathway. This study presents an innovative approach to drug development by integrating machine learning (ML) techniques to identify the potential BACE1 inhibitors. Utilizing a comprehensive dataset of protein-ligand interactions, the current study employed a machine learning model to generate novel ligands with high binding affinity and specificity for the BACE1 active site. The models efficacy was validated through molecular docking studies, which demonstrated superior binding affinities compared to existing FDA-approved inhibitors such as Atabecestat and Lanabecestat. The findings reveal that the top candidate, MLC10, exhibits a unique mechanism of action by promoting controlled flexibility in BACE1, contrasting with the rigid conformations induced by traditional inhibitors. This dynamic modulation enhances the enzymes inhibition, suggesting a promising avenue for therapeutic intervention. Furthermore, solvent-accessible surface area analyses indicate that MLC10 facilitates a more favourable protein conformation, potentially altering catalytic behaviour through increased solvent interactions. This work underscores the transformative potential of machine learning in drug discovery, paving the way for the development of next-generation BACE1 inhibitors. By harnessing computational techniques, the present work aims to address the limitations of current therapies and contribute to more effective treatments for Alzheimers disease, ultimately improving patient outcomes and quality of life.