In silico prediction of chemical aquatic toxicity by multiple machine learning and deep learning approaches.
J Appl Toxicol
; 42(11): 1766-1776, 2022 Nov.
Article
em En
| MEDLINE
| ID: mdl-35653511
ABSTRACT
Fish is one of the model animals used to evaluate the adverse effects of a chemical exposed to the ecosystem. However, its low throughput and relevantly high expense make it impossible to test all new chemicals in manufacture. Hence, using in silico models to prioritize compounds to be tested has been widely applied in environmental risk assessment and drug discovery. In this study, we constructed the local predictive models for four fish species, including bluegill sunfish, rainbow trout, fathead minnow, and sheepshead minnow, and the global models with all four fish data. A total of 1874 unique compounds with their labels, that is, toxic (LC50 < 10 ppm) or nontoxic, were collected from ECOTOX and literature. Both conventional machine learning methods and the deep learning architecture, graph convolutional network (GCN), were used to build predictive models. The classification accuracy of the best local model for each fish species was higher than 0.83. For the global models, two strategies including consistency prediction and probability threshold were adopted to improve the predictive capability at the cost of limiting applicability domain. For 63% of compounds in domain, the accuracy was around 0.97. By comparison of the deep learning and machine learning methods, we found that the single-task GCN showed specific advantages in performance, and multitask GCN showed no advantages over the conventional machine learning methods. The data and models are available on GitHub (https//github.com/ChemPredict/ChemicalAquaticToxicity).
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Cyprinidae
/
Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
J Appl Toxicol
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China