Exploring QSARs of vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase inhibitors by MLR, PLS and PC-ANN

Omar Deeb, Sana Jawabreh, Mohammad Goodarzi

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Quantitative structure-activity relationship study was performed to understand the inhibitory activity of a set of 192 vascular endothelial growth factor receptor-2 (VEGFR-2) compounds. QSAR models were developed using multiple linear regression (MLR) and partial least squares (PLS) as linear methods. While principal component - artificial neural networks (PC-ANN) modeling method with application of eigenvalue ranking factor selection procedure was used as nonlinear method. The results obtained offer good regression models having good prediction ability. The results obtained by MLR and PLS are close and better than those obtained by principal component-artificial neural network. The best model was obtained with a correlation coefficient of 0.87. The strength and the predictive performance of the proposed models was verified using both internal (cross-validation and Y-scrambling) and external statistical validations.

Original languageEnglish (US)
Pages (from-to)2237-2244
Number of pages8
JournalCurrent Pharmaceutical Design
Volume19
Issue number12
DOIs
StatePublished - May 21 2013

Keywords

  • Multiple linear regression (MLR) and Partial least square (PLS)
  • Principal component artificial neural network (PC-ANN)
  • Quantitative structure-activity relationship
  • Vascular endothelial growth factor receptor-2 (VEGFR-2)

ASJC Scopus subject areas

  • Pharmacology
  • Drug Discovery

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