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Application of artificial neural network to predict the retention time of drug metabolites in two‐dimensional liquid chromatography
Author(s) -
Noorizadeh H.,
SobhanArdakani S.,
Raoofi F.,
Noorizadeh M.,
Mortazavi S. S.,
Ahmadi T.,
Pournajafi K.
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.325
Subject(s) - correlation coefficient , artificial neural network , chromatography , cross validation , test set , partial least squares regression , mathematics , artificial intelligence , chemistry , computer science , statistics
Genetic algorithm and partial least square (GA‐PLS) and Levenberg‐ Marquardt artificial neural network (L‐M ANN) techniques were used to investigate the correlation between retention time and descriptors for drug metabolites which obtained by two‐dimensional liquid chromatography. The applied internal (leave‐group‐out cross validation (LGO‐CV)) and external (test set) validation methods were used for the predictive power of four models. Both methods resulted in accurate prediction whereas more accurate results were obtained by L‐M ANN model. The best model obtained from L‐M ANN showed a good R 2 value (determination coefficient between observed and predicted values) for all compounds, which was superior to GA‐PLS models. Copyright © 2011 John Wiley & Sons, Ltd.