QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?
SAR QSAR Environ Res
; 30(7): 507-524, 2019 Jul.
Article
en En
| MEDLINE
| ID: mdl-31244346
The bioconcentration factor (BCF), a key parameter required by the REACH regulation, estimates the tendency for a xenobiotic to concentrate inside living organisms. In silico methods can be valid alternatives to costly data measurements. However, in the industrial context, these theoretical approaches may fail to predict BCF with reasonable accuracy. We analyzed whether models built on public data only have adequate performances when challenged to predict industrial compounds. A new set of 1129 compounds has been collected by merging publicly available datasets. Generative Topographic Mapping was employed to compare this chemical space with a set of new compounds issued from the industry. Some new chemotypes absent in the training set (such as siloxanes) have been detected. A new BCF model has been built using ISIDA (In SIlico design and Data Analysis) fragment descriptors, support vector regression and random forest machine-learning methods. It has been externally validated on: (i) collected data from the literature and (ii) industrial data. The latter also served as benchmark for the freely available tools VEGA, EPISuite, TEST, OPERA. New model performs (RMSE of 0.58 log BCF units) comparably to existing ones but benefits of an extended applicability, covering the industrial set chemical space (78% data coverage).
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Contaminantes Químicos del Agua
/
Simulación por Computador
/
Xenobióticos
/
Relación Estructura-Actividad Cuantitativa
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
Idioma:
En
Revista:
SAR QSAR Environ Res
Asunto de la revista:
SAUDE AMBIENTAL
Año:
2019
Tipo del documento:
Article
País de afiliación:
Francia
Pais de publicación:
Reino Unido