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The purpose of the present study was to develop new statistically validated in silico models to predict the toxicity of organic chemicals using multiple linear regression (MLR) and principal components analysis (PCA). This model applied a diverse set of theoretical molecular descriptors for a set of 468 heterogeneous chemicals with the toxicity data of (LC50)96h in Pimephales promela. The established...
ETSIAT was employed in the quantitative structure-activity relationship (QSAR) modeling on the aliphatic alcohol toxicities to the Tetrahymena, tomato, spider, and bacterial luciferases et al.. The QSAR models were established by partial least squares and further validated by both internal and external validations. The results showed that ETSIAT can characterize the structural information relevant...
Quantitative structure-activity relationship (QSAR) study on the tyrosinase inhibition activities of hydroxy- or methoxy-substituted benzaldoximes and benzaldehyde-O-alkyloximes was performed using support vector machines (SVM) method. The predictive power of the models was verified with the leave one out cross validation test and independent test methods. The cross validation squared correlation...
At present, the multivariate linear regression analysis was adopted mostly in the biological toxicity forecast through establishment equation of the QSAR, but the error forecasted was big in many situations because of the complexity and nonlinearity of structure-activity relationship, and it has a high request to the sample selection, In this paper forecast model of the nitrobenzene compound biological...
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