Noorizadeh H, Sobhan-Ardakani S, Raoofi F, Noorizadeh M, Mortazavi SS, Ahmadi T, Pournajafi K. Application of artificial neural network to predict the retention time of drug metabolites in two-dimensional liquid chromatography.
Drug Test Anal 2011;
5:315-9. [PMID:
22012704 DOI:
10.1002/dta.325]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2011] [Revised: 06/12/2011] [Accepted: 06/13/2011] [Indexed: 11/09/2022]
Abstract
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.
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