In Silico Prediction of Oral Acute Rodent Toxicity Using Consensus Machine Learning.
J Chem Inf Model
; 64(8): 3114-3122, 2024 04 22.
Article
en En
| MEDLINE
| ID: mdl-38498695
ABSTRACT
Acute oral toxicity (AOT) is required for the classification and labeling of chemicals according to the global harmonized system (GHS). Acute oral toxicity studies are optimized to minimize the use of animals. However, with the advent of the three Rs principles and machine learning in toxicology, alternative in silico methods became a reasonable alternative approach for addressing the AOT of new chemical matter. Here, we describe the compilation of AOT data from a commercial database and the development of a consensus classification model after evaluating different combinations of molecular representations and machine learning algorithms. The model shows significantly better performance compared to publicly available AOT models. Its performance was evaluated on an external validation data set, which was compiled from the literature, and an applicability domain was deduced.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Simulación por Computador
/
Aprendizaje Automático
Límite:
Animals
Idioma:
En
Revista:
J Chem Inf Model
Asunto de la revista:
INFORMATICA MEDICA
/
QUIMICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Alemania