Computational design of novel flavonoid analogues as potential AChE inhibitors: analysis using group-based QSAR, molecular docking and molecular dynamics simulations

Abstract

Acetyl cholinesterase (AChE) is an enzyme associated with the loss of cholinergic neurones in Alzheimer’s disease. Acetylcholine is an important neurotransmitter found in the brain and the levels of which decrease significantly in Alzheimer’s patients due to increased expression of AChE. In this study, a novel fragment-based QSAR model has been developed using twenty-seven flavonoid-derived compounds exhibiting inhibitory activity against AChE. This fragment-based method gives the advantage of studying the effect of individual fragments on the biological activity of the compound by evaluating the descriptors. The compounds were divided into training and test sets, where the test set was used for cross-validation of the model. The QSAR model exhibited good statistical values for the training set (r 2 = 0.8070, q 2 = 0.7088, F-ratio = 31.3616) and test set (pred_r 2 = 0.8131). The regression equation obtained had three descriptors describing effect of substitutions in terms of quantitative values. Evaluation of the model implied that electronegative substitution at R1 position lowers the inhibitory activity, while the presence of hydroxyl group improves the same. The presence of rings increased the activity of the compounds. The model thus generated was used to obtain six combinatorial libraries and predicts the activity of these compounds. These compounds were selected for docking and molecular dynamics simulation studies and two leads were identified against AChE.