Improvement of multivariate image analysis applied to quantitative structure-activity relationship (QSAR) analysis by using wavelet-principal component analysis ranking variable selection and least-squares support vector machine regression: QSAR study of checkpoint kinase WEE1 inhibitors

Rodrigo A. Cormanich, Mohammad Goodarzi, Matheus P. Freitas

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Inhibition of tyrosine kinase enzyme WEE1 is an important step for the treatment of cancer. The bioactivities of a series of WEE1 inhibitors have been previously modeled through comparative molecular field analyses (CoMFA and CoMSIA), but a two-dimensional image-based quantitative structure-activity relationship approach has shown to be highly predictive for other compound classes. This method, called multivariate image analysis applied to quantitative structure-activity relationship, was applied here to derive quantitative structure-activity relationship models. Whilst the well-known bilinear and multilinear partial least squares regressions (PLS and N-PLS, respectively) correlated multivariate image analysis descriptors with the corresponding dependent variables only reasonably well, the use of wavelet and principal component ranking as variable selection methods, together with least-squares support vector machine, improved significantly the prediction statistics. These recently implemented mathematical tools, particularly novel in quantitative structure-activity relationship studies, represent an important advance for the development of more predictive quantitative structure-activity relationship models and, consequently, new drugs.

Original languageEnglish (US)
Pages (from-to)244-252
Number of pages9
JournalChemical Biology and Drug Design
Volume73
Issue number2
DOIs
Publication statusPublished - Feb 1 2009

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Keywords

  • MIA-QSAR
  • Regression methods
  • Variable selection
  • WEE1 inhibitors

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine

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