First molecular modeling report on novel arylpyrimidine kynurenine monooxygenase inhibitors through multi-QSAR analysis against Huntington's disease: A proposal to chemists!

Bioorg Med Chem Lett. 2016 Dec 1;26(23):5712-5718. doi: 10.1016/j.bmcl.2016.10.058. Epub 2016 Nov 1.

Abstract

Huntington's disease (HD) is caused by mutation of huntingtin protein (mHtt) leading to neuronal cell death. The mHtt induced toxicity can be rescued by inhibiting the kynurenine monooxygenase (KMO) enzyme. Therefore, KMO is a promising drug target to address the neurodegenerative disorders such as Huntington's diseases. Fiftysix arylpyrimidine KMO inhibitors are structurally explored through regression and classification based multi-QSAR modeling, pharmacophore mapping and molecular docking approaches. Moreover, ten new compounds are proposed and validated through the modeling that may be effective in accelerating Huntington's disease drug discovery efforts.

Keywords: Artificial neural network; Bayesian modeling; Huntington’s disease; Kynurenine monooxygenase; Linear discriminant analysis; Molecular docking; Pharmacophore mapping; Support vector machine.

MeSH terms

  • Bayes Theorem
  • Discriminant Analysis
  • Drug Discovery
  • Enzyme Inhibitors / chemistry*
  • Enzyme Inhibitors / pharmacology*
  • Humans
  • Huntington Disease / drug therapy*
  • Huntington Disease / enzymology
  • Huntington Disease / metabolism
  • Kynurenine 3-Monooxygenase / antagonists & inhibitors*
  • Kynurenine 3-Monooxygenase / metabolism
  • Molecular Docking Simulation
  • Neural Networks, Computer
  • Pyrimidines / chemistry*
  • Pyrimidines / pharmacology*
  • Quantitative Structure-Activity Relationship
  • Support Vector Machine

Substances

  • Enzyme Inhibitors
  • Pyrimidines
  • Kynurenine 3-Monooxygenase