Predicting Toxicity of Aromatic Pollutants Using QSAR Modeling

A large amount of overall organic chemical compounds produced and used annually pertain to aromatic compounds, highly toxic to living organisms in aquatic systems and soil, but to humans too, and moreover, many of them are reported as carcinogenic and mutagenic. One of the most successful approaches for predicting their toxic effect could be found in the application of QSAR/QSPR (quantitative structure-activity/property relationship) modeling. This powerful technique quantitatively relates variations in biological activity, i.e. toxicity, to changes in molecular structure and properties. Hence, the goal of the study was to predict toxicity in vivo of aromatic compounds structured by single benzene ring and including presence and absence of different substitute groups such as hydroxyl-, nitro-, amino-, methyl-, methoxy-, etc, by using QSAR/QSPR tool. A Genetic Algorithm and multiple regression analysis were applied to select the descriptors and to generate the correlation models. Evaluation of models was performed by calculating and comparing their model performances (R2, s, F, Q2) after splitting set of organic compounds to training and test sets. As the most predictive model is shown the 3-variable model having also a good ratio of the number of descriptors and their predictive ability. The main contribution to the toxicity showed descriptors belonging to 2D autocorrelation and atom-centered fragments descriptors, respectively. The GA-MLRA approach showed good results in this study, which allows to built simple, interpretable and transparent model that can be used for future studies of predicting toxicity of organic compounds to mammals