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Titolo:
Robust QSAR models using Bayesian regularized neural networks
Autore:
Burden, FR; Winkler, DA;
Indirizzi:
Monash Univ, Dept Chem, Clayton, Vic 3168, Australia Monash Univ Clayton Vic Australia 3168 Chem, Clayton, Vic 3168, Australia CSIRO, Div Mol Sci, Clayton, Vic 3169, Australia CSIRO Clayton Vic Australia 3169 iv Mol Sci, Clayton, Vic 3169, Australia
Titolo Testata:
JOURNAL OF MEDICINAL CHEMISTRY
fascicolo: 16, volume: 42, anno: 1999,
pagine: 3183 - 3187
SICI:
0022-2623(19990812)42:16<3183:RQMUBR>2.0.ZU;2-4
Fonte:
ISI
Lingua:
ENG
Soggetto:
SELECTIVE MUSCARINIC AGONISTS; CENTRAL-NERVOUS-SYSTEM; BENZODIAZEPINE RECEPTOR; LIGANDS; IMIDAZO<1,2-B>PYRIDAZINES; PHARMACOPHORE; AFFINITY; ANALOGS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
37
Recensione:
Indirizzi per estratti:
Indirizzo: Burden, FR Monash Univ, Dept Chem, Clayton, Vic 3168, Australia Monash Univ Clayton Vic Australia 3168 on, Vic 3168, Australia
Citazione:
F.R. Burden e D.A. Winkler, "Robust QSAR models using Bayesian regularized neural networks", J MED CHEM, 42(16), 1999, pp. 3183-3187

Abstract

We describe the use of Bayesian regularized artificial neural networks (BRANNs) in the development of QSAR models. These networks have the potential to solve a number of problems which arise in QSAR modeling such as: choice of model; robustness of model; choice of validation set; size of validationeffort; and optimization of network architecture. The application of the methods to QSAR of compounds active at the benzoaiazepine and muscarinic receptors is illustrated.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 09/04/20 alle ore 19:26:38