MIA-QSAR modelling of activities of a series of AZT analogues: Bi- and multilinear PLS regression

Mohammad Goodarzi, Matheus Puggina De Freitas

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

The activities of a series of azidothymidine derivatives, compounds with anti-HIV potency, were computationally modelled using multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR). Two regression methods were tested in order to find the best correlation between actual and predicted activities: bilinear (traditional) partial least squares (PLS), applied to the unfolded dataset, and multilinear PLS (N-PLS), applied to the three-way array. The predictive abilities of the PLS- and N-PLS-based models were found to be nearly equivalent, and both the methods derived QSAR models that are statistically superior to conventional QSAR, in which physicochemical descriptors and multiple linear regression were applied.

Original languageEnglish (US)
Pages (from-to)267-272
Number of pages6
JournalMolecular Simulation
Volume36
Issue number4
DOIs
StatePublished - Apr 2010

Keywords

  • AZT analogues
  • HIV
  • MIA-QSAR
  • N-PLS
  • PLS

ASJC Scopus subject areas

  • Chemistry(all)
  • Information Systems
  • Modeling and Simulation
  • Chemical Engineering(all)
  • Materials Science(all)
  • Condensed Matter Physics

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