Prediction of bioactivity of HIV-1 integrase ST inhibitors by multilinear regression analysis and support vector machine.
Bioorg Med Chem Lett
; 23(6): 1648-55, 2013 Mar 15.
Article
em En
| MEDLINE
| ID: mdl-23395655
ABSTRACT
In this study, four computational quantitative structure-activity relationship models were built to predict the biological activity of HIV-1 integrase strand transfer (ST) inhibitors. 551 Inhibitors whose bioactivities were detected by radiolabeling method were collected. The molecules were represented with 20 selected MOE descriptors. All inhibitors were divided into a training set and a test set with two methods:
(1) by a Kohonen's self-organizing map (SOM); (2) by a random selection. For every training set and test set, a multilinear regression (MLR) analysis and a support vector machine (SVM) were used to establish models, respectively. For the test set divided by SOM, the correlation coefficients (rs) were over 0.91, and for the test set split randomly, the rs were over 0.86.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
HIV-1
/
Inibidores de Integrase
/
Integrase de HIV
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Bioorg Med Chem Lett
Assunto da revista:
BIOQUIMICA
/
QUIMICA
Ano de publicação:
2013
Tipo de documento:
Article