Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches - Khan, Mohd Imran; Taehwan, Park; Cho, Yunseong; Scotti, Marcus; Priscila Barros de Menezes, Renata; Husain, Fohad Mabood; Alomar, Suliman Yousef; Baig, Mohammad Hassan; Dong, Jae-June (Frontiers in Neuroscience, 2023)
Affecting 40–50 million of the global population, Alzheimer's is the most studied progressive neurodegenerative disorder. 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 inhibitors retains a great research focus for anti-Alzheimer's drug discovery. This study focused on finding acetylcholinesterase inhibitors by applying the Machine Learning predictive modeling approach, which is an integral part of the current drug discovery process. In this study, we have extensively utilized machine learning (ML) and other in silico approaches to search for an effective lead molecule against acetylcholinesterase. The output of this study helped us to identify some promising acetylcholinesterase inhibitors. The selected compounds performed well at a different level of analysis and may provide a possible pathway for the future design of potent acetylcholinesterase inhibitors.