Application of genetic algorithm‐kernel partial least square as a novel non‐linear feature selection method: partitioning of drug molecules. Issue 2 (25th March 2011)

• Record Type: Journal Article
• Authors: Noorizadeh, H.; Sobhan Ardakani, S.; Ahmadi, T.; Mortazavi, S. S.; Noorizadeh, M.
• Is Part Of: Drug testing and analysis. Volume 5:Issue 2(2013:Feb.)
• Page Start: 89
• Page End: 95
• Publication Date: 2011-03-25
• Subjects: Drugs -- Analysis -- Periodicals; Drug testing -- Periodicals; Chemistry, Forensic -- Periodicals; 615.1901
• DOI: 10.1002/dta.275
• Languages: English
• ISSNs: 1942-7603

Abstract

<abstract abstract-type="main" xml:lang="en" id="dta275-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="dta275-para-0001">Genetic algorithm (GA) and partial least squares (PLS) and kernel PLS (KPLS) techniques were used to investigate the correlation between immobilized liposome chromatography partitioning (log Ks) and descriptors for 65 drug compounds. The models were validated using leave‐group‐out cross validation LGO‐CV. The results indicate that GA‐KPLS can be used as an alternative modelling tool for quantitative structure‐property relationship (QSPR) studies. Copyright © 2011 John Wiley &amp; Sons, Ltd.</p> </abstract>