HIV-1 reverse transcriptase inhibitor design using artificial neural networks.
J Med Chem
; 37(16): 2520-6, 1994 Aug 05.
Article
em En
| MEDLINE
| ID: mdl-7520081
ABSTRACT
Artificial neural networks were used to analyze and predict the human immunodeficiency virus type 1 reverse transcriptase inhibitors. The training and control sets included 44 molecules (most of them are well-known substances such as AZT, dde, etc.). The activities of the molecules were taken from literature. Topological indices were calculated and used as molecular parameters. The four most informative parameters were chosen and applied to predict activities of both new and control molecules. We used a network pruning algorithm and network ensembles to obtain the final classifier. Increasing of neural network generalization of the new data was observed, when using the aforementioned methods. The prognosis of new molecules revealed one molecule as possibly very active. It was confirmed by further biological tests.
Buscar no Google
Base de dados:
MEDLINE
Assunto principal:
Pirimidinas
/
Desenho de Fármacos
/
Redes Neurais de Computação
/
Inibidores da Transcriptase Reversa
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
1994
Tipo de documento:
Article