QSPR models for predicting log P(liver) values for volatile organic compounds combining statistical methods and domain knowledge.
Molecules
; 17(12): 14937-53, 2012 Dec 17.
Article
en En
| MEDLINE
| ID: mdl-23247367
ABSTRACT
Volatile organic compounds (VOCs) are contained in a variety of chemicals that can be found in household products and may have undesirable effects on health. Thereby, it is important to model blood-to-liver partition coefficients (log P(liver)) for VOCs in a fast and inexpensive way. In this paper, we present two new quantitative structure-property relationship (QSPR) models for the prediction of log P(liver), where we also propose a hybrid approach for the selection of the descriptors. This hybrid methodology combines a machine learning method with a manual selection based on expert knowledge. This allows obtaining a set of descriptors that is interpretable in physicochemical terms. Our regression models were trained using decision trees and neural networks and validated using an external test set. Results show high prediction accuracy compared to previous log P(liver) models, and the descriptor selection approach provides a means to get a small set of descriptors that is in agreement with theoretical understanding of the target property.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Relación Estructura-Actividad Cuantitativa
/
Compuestos Orgánicos Volátiles
/
Gases
/
Modelos Teóricos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Molecules
Asunto de la revista:
BIOLOGIA
Año:
2012
Tipo del documento:
Article
País de afiliación:
Argentina