Use of automatic relevance determination in QSAR studies using Bayesian neural networks.
J Chem Inf Comput Sci
; 40(6): 1423-30, 2000.
Article
em En
| MEDLINE
| ID: mdl-11128101
ABSTRACT
We describe the use of Bayesian regularized artificial neural networks (BRANNs) coupled with automatic relevance determination (ARD) in the development of quantitative structure-activity relationship (QSAR) models. These BRANN-ARD networks have the potential to solve a number of problems which arise in QSAR modeling such as the following choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The ARD method ensures that irrelevant or highly correlated indices used in the modeling are neglected as well as showing which are the most important variables in modeling the activity data. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors as well as some toxicological data of the effect of substituted benzenes on Tetetrahymena pyriformis is illustrated.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
J Chem Inf Comput Sci
Ano de publicação:
2000
Tipo de documento:
Article
País de afiliação:
Austrália