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Research article
pKa modelling and prediction of drug molecules through GA-KPLS and L-M ANN
Article first published online: 16 APR 2011
DOI: 10.1002/dta.279
Copyright © 2011 John Wiley & Sons, Ltd.
Additional Information
How to Cite
Noorizadeh, H., Farmany, A. and Noorizadeh, M. (2013), pKa modelling and prediction of drug molecules through GA-KPLS and L-M ANN. Drug Test Analysis, 5: 103–109. doi: 10.1002/dta.279
Publication History
- Issue published online: 13 FEB 2013
- Article first published online: 16 APR 2011
- Manuscript Accepted: 19 FEB 2011
- Manuscript Revised: 16 FEB 2011
- Manuscript Received: 16 JAN 2011
- Abstract
- Article
- References
- Cited By
Keywords:
- drug molecules;
- immobilized liposome chromatography;
- QSPR;
- genetic algorithm-kernel partial least squares;
- Levenberg-Marquardt artificial neural network
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 (pKa) 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.