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pK a modelling and prediction of drug molecules through GA‐KPLS and L‐M ANN
Author(s) -
Noorizadeh H.,
Farmany A.,
Noorizadeh M.
Publication year - 2013
Publication title -
drug testing and analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.065
H-Index - 54
eISSN - 1942-7611
pISSN - 1942-7603
DOI - 10.1002/dta.279
Subject(s) - quantitative structure–activity relationship , artificial neural network , cross validation , dissociation (chemistry) , kernel (algebra) , test set , artificial intelligence , molecular descriptor , biological system , dissociation constant , genetic algorithm , computer science , chemistry , mathematics , machine learning , combinatorics , biochemistry , receptor , biology
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 & Sons, Ltd.