QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions

Mohammad Goodarzi, Richard Jensen, Yvan Vander Heyden

Research output: Contribution to journalArticle

42 Scopus citations

Abstract

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logkw). The overall best model was the SVM one built using descriptors selected by ACO.

Original languageEnglish (US)
Pages (from-to)84-94
Number of pages11
JournalJournal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences
Volume910
DOIs
Publication statusPublished - Feb 15 2012

    Fingerprint

Keywords

  • ACO
  • Chromatographic retention
  • MLR
  • QSRR
  • Relief method
  • SVM

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Clinical Biochemistry
  • Cell Biology

Cite this