Augmented three-mode MIA-QSAR modeling for a series of anti-HIV-1 compounds

Mohammad Goodarzi, Matheus P. Freitas

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

The bioactivities of a series of 2-amino-6-arylsulfonylbenzonitriles and their thio- and sulfinyl congeners have been previously modeled by using the MIA-QSAR approach and the PLS regression method [10]. The present work reports the significant improvement in the prediction ability of the Multivariate Image Analysis applied to Quantitative Structure-Activity Relationship (MIA -QSAR) model by applying Parallel Factor Analysis (PARAFAC) and Artificial Neural Network (ANN) directly to the three-way array built. This perspective represents an important advance for the accurate prediction of potential drug candidates.

Original languageEnglish (US)
Pages (from-to)1092-1097
Number of pages6
JournalQSAR and Combinatorial Science
Volume27
Issue number9
DOIs
StatePublished - Sep 1 2008

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Quantitative Structure-Activity Relationship
HIV-1
Factor analysis
Bioactivity
Image analysis
Statistical Factor Analysis
Multivariate Analysis
Neural networks
Pharmaceutical Preparations

Keywords

  • ANN
  • Anti-HIV-1 compounds
  • MIA-QSAR
  • PARAFAC

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications
  • Organic Chemistry

Cite this

Augmented three-mode MIA-QSAR modeling for a series of anti-HIV-1 compounds. / Goodarzi, Mohammad; Freitas, Matheus P.

In: QSAR and Combinatorial Science, Vol. 27, No. 9, 01.09.2008, p. 1092-1097.

Research output: Contribution to journalArticle

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