New hybrid genetic based support vector regression as QSAR approach for analyzing flavonoids-GABA(A) complexes

Mohammad Goodarzi, Pablo R. Duchowicz, Chih H. Wu, Francisco M. Fernández, Eduardo A. Castro

Research output: Contribution to journalArticle

52 Scopus citations

Abstract

Several studies were conducted in past years which used the evolutionary process of Genetic Algorithms for optimizing the Support Vector Regression parameter values although, however, few of them were devoted to the simultaneously optimization of the type of kernel function involved in the established model. The present work introduces a new hybrid genetic-based Support Vector Regression approach, whose statistical quality and predictive capability is afterward analyzed and compared to other standard chemometric techniques, such as Partial Least Squares, Back-Propagation Artificial Neural Networks, and Support Vector Machines based on Cross-Validation. For this purpose, we employ a data set of experimentally determined binding affinity constants toward the benzodiazepine binding site of the GABA (A) receptor complex on 78 flavonoid ligands.

Original languageEnglish (US)
Pages (from-to)1475-1485
Number of pages11
JournalJournal of Chemical Information and Modeling
Volume49
Issue number6
DOIs
StatePublished - Jun 22 2009

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

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