Study of modeling and optimization for predicting the acute toxicity of carbamate pesticides using the binding information with carrier protein.
Spectrochim Acta A Mol Biomol Spectrosc
; 273: 121038, 2022 May 15.
Article
em En
| MEDLINE
| ID: mdl-35189491
ABSTRACT
To predict drug acute toxicity using the binding information with human serum albumin, our research group established a new method (Carrier protein binding information-toxicity relationship, CPBITR). Unfortunately, the previous model had too few data sets which may affect the accuracy and credibility of the model. In this paper, therefore, we measured the binding modes of three carbamate pesticides, Bendiocarb, Butocarboxim and Dioxacarb with human serum albumin (HSA) to supplement the previously modeled training set. Multispectral methods and molecular docking were used to study their binding modes. We built and optimized the previous models with the combined information of three different toxicity pesticides and HSA in order to find better prediction method. The results showed that Back-propagation Artificial Neural Network model has the best fitting effect among these models. In conclusion, the proposed model effectively improves the accuracy and credibility of the existing model. It results in significant predict drug acute toxicity using the binding information with carrier protein and contribute to drug development and research.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Praguicidas
/
Proteínas de Transporte
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Spectrochim Acta A Mol Biomol Spectrosc
Ano de publicação:
2022
Tipo de documento:
Article