Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305.
Bioaccumulation
Bioconcentration factor
Chemicals regulations
Organisation for economic co-operation and development 305
Quantitative read-across structure-activity relationship
Quantitative structure-activity relationship
Read-across
Journal
Journal of hazardous materials
ISSN: 1873-3336
Titre abrégé: J Hazard Mater
Pays: Netherlands
ID NLM: 9422688
Informations de publication
Date de publication:
03 Sep 2024
03 Sep 2024
Historique:
received:
31
05
2024
revised:
31
08
2024
accepted:
31
08
2024
medline:
8
9
2024
pubmed:
8
9
2024
entrez:
7
9
2024
Statut:
aheadofprint
Résumé
In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds tested using Organisation for Economic Co-operation and Development Test Guideline 305 for bioaccumulation in fish. Initially, we constructed the q-RASAR model using the partial least squares regression method, yielding promising statistical results for the training set (R
Identifiants
pubmed: 39243539
pii: S0304-3894(24)02304-5
doi: 10.1016/j.jhazmat.2024.135725
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
135725Informations de copyright
Copyright © 2024 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.