Benzimidazole Derivatives in Identifying Novel Acetylcholinesterase Inhibitors: A Combination of 3D-QSAR, Docking and Molecular Dynamics Simulation

Acetylcholinesterase is a promising therapeutic candidate for the treatment of neurodegenerative disorders, acetylcholine dysfunction, and other cognitive problems. In the current study, a 3D-QSAR approach was applied to a series of benzimidazole derivatives to reveal the key influencing factors contributing to their acetylcholinesterase inhibition activity and selectivity. The developed two models, CoMFA and CoMSIA, were found to be internally validated using a training set of compounds, and both models demonstrated significant statistical reliability. Contour maps of developed models were employed to examine the main structural characteristics of inhibitors that affected their potency. It was found that electrostatic and hydrophobic interactions are significantly important for improving the inhibitory activities, leading to the design of four novel acetylcholinesterase inhibitors. Among the newly designed compounds, compound A1 with the highest predicted activity was subjected to detailed molecular docking and compared to the most active compound. Furthermore, 100 ns molecular dynamics (MD) simulation was conducted to explore the binding modes and conformational modifications throughout the interaction of compound A1 and acetylcholinesterase. The docking and MD simulation findings showed that the newly designed compound A1 remained stable within the active site of the identified acetylcholinesterase receptor, demonstrating its promising role as a new potential acetylcholinesterase drug candidate.