Multiple linear regression analysis and optimal descriptors: Predicting the cholesteryl ester transfer protein inhibition activity

Bakhtiyor F. Rasulev, Andrey A. Toropov, Ashton T. Hamme, Jerzy Leszczynski

Research output: Contribution to journalArticle

Abstract

Quantitative structure - activity relationships have been developed for a set of 40 halogenated substituted N-benzyl-N-phenyl aminoalcohol compounds. IC50 values for Cholesteryl Ester Transfer Protein (CETP) inhibition activity for these compounds expressed in log units have been modeled by Multiple Linear Regression Analysis (MLRA) based on descriptors generated by DRAGON software and optimal descriptors approach. Forty benzene derivatives have been divided into training (n=20) and test (n=20) sets. In the case of the MLRA, the three-variable model has the best predictive potential. Comparison of the quality of MLRA and optimal descriptor models shows that the predictive potential of the one-variable model, based on the optimal descriptor, provides a low correlation coefficient in the training set. However, this one-variable model performs better for the test set when compared to the two- and three-variable MLRA models.

Original languageEnglish
Pages (from-to)595-606
Number of pages12
JournalQSAR and Combinatorial Science
Volume27
Issue number5
DOIs
Publication statusPublished - May 2008

Keywords

  • Cholesteryl ester transfer protein
  • Inhibitors
  • MLRA
  • Optimal descriptors
  • QSAR

ASJC Scopus subject areas

  • Drug Discovery
  • Organic Chemistry
  • Computer Science Applications

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