Predicting blood-brain barrier penetration of drugs using an artificial neural network.
Pharmazie
; 59(2): 126-30, 2004 Feb.
Article
em En
| MEDLINE
| ID: mdl-15025181
ABSTRACT
An artificial neural network model is developed to predict the ratios of the steady-state concentrations of drugs in the brain to those in the blood (log BB) from their molecular structural parameters. These molecular structural parameters are the molecular volume (V), the sum of the absolute values of the net atomic charges of oxygen and nitrogen atoms which are hydrogen-bond acceptors (Q(O, N)), and the sum of the net atomic charges of hydrogen atoms attached to oxygen or nitrogen atoms (Q(H)). For a training set of 56 compounds and a test set of 5 compounds, root mean squared errors (RMSE) between experimental log BB values and calculated/predicted log BB values were 0.236 and 0.258, respectively. These molecular structural parameters can be obtained easily from quantum chemical calculations. The model is suitable for the rapid prediction of the blood-brain barrier penetration of drugs.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Preparações Farmacêuticas
/
Barreira Hematoencefálica
/
Redes Neurais de Computação
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Pharmazie
Assunto da revista:
FARMACIA
Ano de publicação:
2004
Tipo de documento:
Article