Application of artificial neural network to predict the retention time of drug metabolites in two-dimensional liquid chromatography.
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.
Department of Chemistry, Ilam Branch, Islamic Azad University, Ilam, Iran. hadinoorizadeh@yahoo.com
H
H
Noorizadeh
S
S
Sobhan-Ardakani
F
F
Raoofi
M
M
Noorizadeh
S S
SS
Mortazavi
T
T
Ahmadi
K
K
Pournajafi
D000465:Algorithms; D002853:Chromatography, Liquid / Q000379:methods*; D016018:Least-Squares Analysis; D008956:Models, Chemical; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000302:isolation & purification* / Q000378:metabolism*
D004364:Pharmaceutical Preparations