‘Ideal correlations’ for the predictive toxicity to <i>Tetrahymena pyriformis</i>

Predictive models for toxicity to <i>Tetrahymena pyriformis</i> are an important component of natural sciences. The present study aims to build up a predictive model for the endpoint using the so-called index of ideality of correlation (<i>IIC</i>). Besides, the comparison of the predictive potential of these models with the predictive potential of models suggested in the literature is the task of the present study. The Monte Carlo technique is a tool to build up the predictive model applied in this study. The molecular structure is represented via a simplified molecular input-line entry system (SMILES). The <i>IIC</i> is a statistical characteristic sensitive to both the correlation coefficient and mean absolute error. Applying of the <i>IIC</i> to build up quantitative structure–activity relationships (QSARs) for the toxicity to <i>Tetrahymena pyriformis</i> improves the predictive potential of those models for random splits into the training set and the validation set. The calculation was carried out with CORAL software (http://www.insilico.eu/coral). The statistical quality of the suggested models is incredibly good for the external validation set, but the statistical quality of the models for the training set is modest. This is the paradox of ideal correlation, which is obtained with applying the <i>IIC.</i> The Monte Carlo technique is a convenient and reliable way to build up a predictive model for toxicity to <i>Tetrahymena pyriformis</i>. The <i>IIC</i> is a useful statistical criterion for building up predictive models as well as for the assessment of their statistical quality.