Alzheimer's disease (AD) is a neurodegenerative disease characterized by a low acetylcholine (ACh) concentration in the hippocampus and cortex. ACh is a neurotransmitter hydrolyzed by acetylcholinesterase (AChE). Therefore, it is not surprising that AChE inhibitors (AChEIs) have shown better results in the treatment of AD than any other strategy. To improve the effects of AD, many researchers have focused on designing and testing new AChEIs. One of the principal strategies has been the use of computational methods (structural bioinformatics or in silico methods).
In this review, we summarize the in silico methods used to enhance the understanding of AChE, particularly at the binding site, to design new AChEIs. Several computational methods have been used, such as docking approaches, molecular dynamics studies, quantum mechanical studies, electronic properties, hindrance effects, partition coefficients (Log P) and molecular electrostatic potentials surfaces, among other physicochemical methods that exhibit quantitative structure-activity relationships.
Keywords: In silico methods, docking, acetylcholinesterase, molecular dynamics, sequence alignment, Alzheimer's disease, AChEIs, hindrance effects, SAR, Protein Data Bank
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