On the use of PLS and N-PLS in MIA-QSAR: Azole antifungals

Mohammad Goodarzi, Matheus P. Freitas

Research output: Contribution to journalArticle

27 Scopus citations

Abstract

The antifungal activities of a series of azole derivatives have been modeled by using MIA (multivariate image analysis) descriptors. Two regression methods were applied to correlate such descriptors with the activities column vector: bilinear (classical) and multilinear (N-way) partial least squares - PLS and N-PLS, respectively. The PLS-based model for this series of compounds demonstrated higher predictive ability than the N-PLS-based model, in opposition to some published results for other series of compounds. The activities block was taken in logarithmic scale (pMIC90(cpd)/pMIC90(bifonazole)) and the statistical performance of both models was found to be significantly better than the CoMFA analysis previously established.

Original languageEnglish (US)
Pages (from-to)59-62
Number of pages4
JournalChemometrics and Intelligent Laboratory Systems
Volume96
Issue number1
DOIs
StatePublished - Mar 15 2009

Keywords

  • Antifungals
  • MIA-QSAR
  • N-PLS regression
  • PLS regression

ASJC Scopus subject areas

  • Analytical Chemistry
  • Software
  • Process Chemistry and Technology
  • Spectroscopy
  • Computer Science Applications

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