Towards better understanding of feature-selection or reduction techniques for Quantitative Structure-Activity Relationship models

Mohammad Goodarzi, Yvan Vander Heyden, Simona Funar-Timofei

Research output: Contribution to journalReview article

42 Scopus citations

Abstract

A Quantitative Structure-Activity Relationship (QSAR) is a linear or non-linear model, which relates variations in molecular descriptors to variations in the biological activity of a series of active and/or inactive molecules. For this article, different feature-selection or reduction methods were all coupled with Partial Least Squares (PLS) modeling during the selection of features. A PLS model was also built with the entire set of molecular descriptors and was used as a reference to check the reliability and the performance of the different feature-selection methods. To evaluate the ability of the different feature-selection methods, they were performed on two data sets.

Original languageEnglish (US)
Pages (from-to)49-63
Number of pages15
JournalTrAC - Trends in Analytical Chemistry
Volume42
DOIs
StatePublished - Jan 2013

Keywords

  • Aldose-reductase inhibitor
  • Biological activity
  • Biological property
  • Feature reduction
  • Feature selection
  • Molecular descriptor
  • Multiple Linear Regression (MLR)
  • Partial Least Squares (PLS)
  • Quantitative Structure-Activity Relationship (QSAR)
  • Rho kinase (ROCK) inhibitor

ASJC Scopus subject areas

  • Analytical Chemistry
  • Spectroscopy

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