pKa modelling and prediction of drug molecules through GA-KPLS and L-M ANN
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| Deutscher übersetzter Titel: | pKa-Modellierung und Prognose von Wirkstoffmolekülen durch GA-KPLS und L-M ANN |
|---|---|
| Autor: | Noorizadeh, H. ; Farmany, A.; Noorizadeh, M. |
| Erschienen in: | Drug testing and analysis |
| Veröffentlicht: | 5 (2013), 1/2, S. 103-109, Lit. |
| Format: | Literatur (SPOLIT) |
| Publikationstyp: | Zeitschriftenartikel |
| Medienart: | Gedruckte Ressource Elektronische Ressource (online) |
| Sprache: | Englisch |
| ISSN: | 1942-7603, 1942-7611 |
| DOI: | 10.1002/dta.279 |
| Schlagworte: | |
| Online Zugang: | |
| Erfassungsnummer: | PU201304002838 |
| Quelle: | BISp |
Abstract
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. Verf.-Referat