Classification of anti-HIV compounds using counterpropagation artificial neural networks and decision trees.
SAR QSAR Environ Res
; 22(7-8): 639-60, 2011 Oct.
Article
en En
| MEDLINE
| ID: mdl-21999803
ABSTRACT
The main aim of the present work was to collect and categorize anti-HIV molecules in order to identify general structure-activity relationships. In this respect, a total of 5580 drugs and drug-like molecules was collected from 256 different articles published between 1992 and 2010. An algorithm called genetic algorithm-pattern search counterpropagation artificial neural networks (GPS-CPANN) was proposed for the classification of compounds. In addition, the CART (classification and regression trees) method was used for construction of decision trees and finding the best molecular descriptors. The results revealed that the developed CPANN models and decision tree can correctly classify the molecules according to their inhibition mechanisms and activities. Some general parameters such as molecular weight, average molecular weight, number of hydrogen atoms and number of hydroxyl groups were found to be important for describing the inhibition behaviour of anti-HIV agents. The developed classifier models in this work can be used to screen large libraries of compounds to identify those likely to display activity as anti-HIV agents.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_financiamento_saude
Asunto principal:
Relación Estructura-Actividad
/
Árboles de Decisión
/
Redes Neurales de la Computación
/
Fármacos Anti-VIH
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
Idioma:
En
Revista:
SAR QSAR Environ Res
Asunto de la revista:
SAUDE AMBIENTAL
Año:
2011
Tipo del documento:
Article
País de afiliación:
Irán