Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives

  • Shuqun Zhang
  • , Bo Hou
  • , Huaiyu Yang
  • , Zhili Zuo*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer’s disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r2 = 0.988, q2 = 0.757, ONC = 6; r2 = 0.966, q2 = 0.645, ONC = 5; and r2 = 0.957, q2 = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r2values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.

Original languageEnglish
Pages (from-to)591-602
Number of pages12
JournalArchives of Pharmacal Research
Volume39
Issue number5
DOIs
StatePublished - 1 May 2016
Externally publishedYes

Keywords

  • AChE
  • AD
  • CoMFA
  • CoMSIA
  • HQSAR
  • Huprines inhibitors

Fingerprint

Dive into the research topics of 'Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives'. Together they form a unique fingerprint.

Cite this