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

Fang Zheng, Max Zhan, Xiaoqin Huang, Mohamed Diwan M. Abdul Hameed, Chang Guo Zhan

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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.

Original languageEnglish
Pages (from-to)538-549
Number of pages12
JournalBioorganic and Medicinal Chemistry
Issue number1
StatePublished - Jan 1 2014

Bibliographical note

Funding Information:
This work was supported by NIH Grants R01DA013930 , R01DA032910 , R01DA035552 , and NSF Grant CHE-1111761 . M.Z. is grateful to the National Institute on Drug Abuse (NIDA) of the NIH for a scholarship award from the 2013 Summer Research with NIDA Program and to the Kentucky Young Research Program (KYRP) for a research grant. M.Z. worked at the University of Kentucky as a student from the Math, Science and Technology Center (MSTC) program at Paul L. Dunbar High School, Lexington, KY. The authors also acknowledge the Computer Center at the University of Kentucky for supercomputing time on a Dell X-series Cluster with 384 nodes or 4768 processors.


  • Enzyme inhibitor
  • Molecular docking
  • Neural network analysis
  • QSAR

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Molecular Biology
  • Pharmaceutical Science
  • Drug Discovery
  • Clinical Biochemistry
  • Organic Chemistry


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