Modeling in vitro inhibition of butyrylcholinesterase using molecular docking, multi-linear regression and artificial neural network approaches

Butyrylcholinesterase (BChE) has been an important protein used for development of anti-cocaine medication. Through computational design, BChE mutants with ∼2000-fold improved catalytic efficiency against cocaine have been discovered in our lab. To study drug-enzyme interaction it is important to build mathematical model to predict molecular inhibitory activity against BChE. This report presents a neural network (NN) QSAR study, compared with multi-linear regression (MLR) and molecular docking, on a set of 93 small molecules that act as inhibitors of BChE by use of the inhibitory activities (pIC₅₀ values) of the molecules as target values. The statistical results for the linear model built from docking generated energy descriptors were: r² = 0.67, rmsd = 0.87, q² = 0.65 and loormsd = 0.90; the statistical results for the ligand-based MLR model were: r² = 0.89, rmsd = 0.51, q² = 0.85 and loormsd = 0.58; the statistical results for the ligand-based NN model were the best: r² = 0.95, rmsd = 0.33, q² = 0.90 and loormsd = 0.48, demonstrating that the NN is powerful in analysis of a set of complicated data. As BChE is also an established drug target to develop new treatment for Alzheimer's disease (AD). The developed QSAR models provide tools for rationalizing identification of potential BChE inhibitors or selection of compounds for synthesis in the discovery of novel effective inhibitors of BChE in the future.