Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches.

Abstract

Alzheimer's disease (AD) is the most studied progressive eurodegenerative disorder, affecting 40-50 million of the global population. This progressive neurodegenerative disease is marked by gradual and irreversible declines in cognitive functions. The unavailability of therapeutic drug candidates restricting/reversing the progression of this dementia has severed the existing challenge. The development of acetylcholinesterase (AChE) inhibitors retains a great research focus for the discovery of an anti-Alzheimer drug. This study focused on finding AChE inhibitors by applying the machine learning (ML) predictive modeling approach, which is an integral part of the current drug discovery process. In this study, we have extensively utilized ML and other in silico approaches to search for an effective lead molecule against AChE. The output of this study helped us to identify some promising AChE inhibitors. The selected compounds performed well at different levels of analysis and may provide a possible pathway for the future design of potent AChE inhibitors.