PKa modelling and prediction of drug molecules through GA‐KPLS and L‐M ANN. Issue 2 (16th April 2011)

• Record Type: Journal Article
• Authors: Noorizadeh, H.; Farmany, A.; Noorizadeh, M.
• Is Part Of: Drug testing and analysis. Volume 5:Issue 2(2013:Feb.)
• Page Start: 103
• Page End: 109
• Publication Date: 2011-04-16
• Subjects: Drugs -- Analysis -- Periodicals; Drug testing -- Periodicals; Chemistry, Forensic -- Periodicals; 615.1901
• DOI: 10.1002/dta.279
• Languages: English
• ISSNs: 1942-7603

Abstract

<abstract abstract-type="main" xml:lang="en" id="dta279-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="dta279-para-0001">Genetic algorithm and partial least square (GA‐PLS), kernel PLS (GA‐KPLS) and Levenberg‐ Marquardt artificial neural network (L‐M ANN) techniques were used to investigate the correlation between dissociation constant (pK_a) and descriptors for 60 drug compounds. The applied internal (leave‐group‐out cross validation (LGO‐CV)) and external (test set) validation methods were used for the predictive power of models. Descriptors of GA‐KPLS model were selected as inputs in L‐M ANN model. The results indicate that L‐M ANN can be used as an alternative modeling tool for quantitative structure–property relationship (QSPR) studies. Copyright © 2011 John Wiley &amp; Sons, Ltd.</p> </abstract>