On November 30, 2022, Dieguez-Santana, Karel; Nachimba-Mayanchi, Manuel Mesias; Puris, Amilkar; Gutierrez, Roldan Torres; Gonzalez-Diaz, Humberto published an article.Computed Properties of 4719-04-4 The title of the article was Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches. And the article contained the following:
Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesticides has received increasing attention from regulatory agencies due to their various acute and chronic effects on multiple organisms. In this study, Quant. Structure-Toxicity Relationship (QSTR) models were established using Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict pesticide toxicity in Americamysis bahia. The most influential descriptors included in the MLR model are RBF, JGI2, nCbH, nRCOOR, nRSR, nPO4 and ‘Cl-090′, with pos. contributions to the dependent variable (neg. decimal logarithm of median lethal concentration at 96-h). The Random Forest (RF) regression model was superior amongst the five ML models. We observed higher values of R2 (0.812) and lower values of RMSE (0.595) and MAE (0.462) in the cross-validation training set and external validation set. Similarly, this study had a high level of fitness and was internally robust and externally predictive compared to models presented in similar studies. The results suggest that the developed QSTR models are suitable for reliably predicting the aquatic toxicity of structurally diverse pesticides and can be used for screening, prioritising new pesticides, filling data gaps and overcoming the limitations of in vivo and in vitro tests. The experimental process involved the reaction of 2,2’,2”-(1,3,5-Triazinane-1,3,5-triyl)triethanol(cas: 4719-04-4).Computed Properties of 4719-04-4
The Article related to pesticide acute toxicity americamysis bahia linear nonlinear qstr modeling, aquatic toxicity, machine learning, multiple linear regression, quantitative structure–toxicity relationship, random forest and other aspects.Computed Properties of 4719-04-4
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