Abstract
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 (LC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> ) <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">96h</sub> in Pimephales promela. The established in silico models have a reasonable statistical performance (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> >; 0.6 and Q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> >; 0.6) for predicting the toxic compounds.
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