Efficient and knowledge-based hierarchal virtual screening applied to identify potential inhibitors of cholinesterase enzyme

Cholinesterases (ChEs) are pivotal in the pathophysiology of various neuromuscular diseases, including Parkinson’s disease, Alzheimer’s disease, myasthenia gravis, and vascular dementia. The involvement of ChEs in open-angle glaucoma establishes them as promising drug targets. This study employed hierarchical virtual screening (HVS) to identify lead compounds against cholinesterase drug targets. A four-step, knowledge-based, and time-efficient HVS protocol was implemented, resulting in the identification of 41 novel scaffolds capable of evolving into diverse functional ChE inhibitors. This includes inhibitors with selectivity for acetylcholinesterase (AChE) or butyrylcholinesterase (BChE), dual-target, and dual-binding-site inhibitors. The proposed HVS scheme integrates structure-based pharmacophores, docking methodologies, and physicochemical-descriptor-based filters. Among the identified scaffolds, 13 potential ChE inhibitors were selected based on their non-covalent interactions with key binding site residues of both AChE and BChE. Furthermore, an assessment of the physicochemical and pharmacokinetic profiles of these compounds was conducted. The selected potential inhibitors are recommended for further evaluation through in vitro and in vivo assay studies. This comprehensive approach enhances the prospects of identifying effective therapeutic agents targeting cholinesterases in neuromuscular diseases.