QSPR models for estimating retention in HPLC with the p solute polarity parameter based on the Monte Carlo method

The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models

Acetylcholinesterase (AChE) inhibitors, dihydrofolate reductase inhibitors (DHFR), Toxicity in Tetrahymena pyriformis (TP), Acute Toxicity in fathead minnow (TFat), Water solubility (WS), and Acute Aquatic Toxicity in Daphnia magna (DM) are examined as endpoints to establish quantitative structure - property/activity relationships (QSPRs/QSARs). The Index of Ideality of Correlation (IIC) is a measure of predictive potential. The IIC has been studied in a few recent works. The comparison of models for the six endpoints above confirms that the index can be a useful tool for building up and validation of QSPR/QSAR models. All examined endpoints are important from an ecologic point of view. The diversity of examined endpoints confirms that the IIC is real criterion of the predictive potential of a model.

Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential

Mutagenicity is the ability of a substance to induce mutations. This hazardous ability of a substance is decisive from point of view of ecotoxicology. The number of substances, which are used for practical needs, grows every year. Consequently, methods for at least preliminary estimation of mutagenic potential of new substances are necessary. Semi-correlations are a special case of traditional correlations. These correlations can be named as "correlations along two parallel lines." This kind of correlation has been tested as a tool to predict selected endpoints, which are represented by only two values: "inactive/active" (0/1). Here this approach is used to build up predictive models for mutagenicity of large dataset (n = 3979). The so-called index of ideality of correlation (IIC) has been tested as a statistical criterion to estimate the semi-correlation. Three random splits of experimental data into the training, invisible-training, calibration, and validation sets were analyzed. Two models were built up for each split: the first model based on optimization without the IIC and the second model based on optimization where IIC is involved in the Monte Carlo optimization. The statistical characteristics of the best model (calculated with taking into account the IIC) n = 969; sensitivity = 0.8050; specificity = 0.9069; accuracy = 0.8648; Matthews's correlation coefficient = 0.7196 (using IIC). Thus, the use of IIC improves the statistical quality of the binary classification models of mutagenic potentials (Ames test) of organic compounds.