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Updated: Mar 8, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Prediction of biodegradability of aromatics in water using QSAR modeling
Matija Cvetnic1, Daria Juretic Perisic1, Marin Kovacic1
1Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev trg 19, Zagreb 10000, Croatia.
Abstract:
The study was aimed at developing models for predicting the biodegradability of aromatic water pollutants. For that purpose, 36 single-benzene ring compounds, with different type, number and position of substituents, were used. The biodegradability was estimated according to the ratio of the biochemical (BOD5) and chemical (COD) oxygen demand values determined for parent compounds ((BOD5/COD)0), as well as for their reaction mixtures in half-life achieved by UV-C/H2O2 process ((BOD5/COD)t1/2). The models correlating biodegradability and molecular structure characteristics of studied pollutants were derived using quantitative structure-activity relationship (QSAR) principles and tools. Upon derivation of the models and calibration on the training and subsequent testing on the test set, 3- and 5-variable models were selected as the most predictive for (BOD5/COD)0 and (BOD5/COD)t1/2, respectively, according to the values of statistical parameters R2 and Q2. Hence, 3-variable model predicting (BOD5/COD)0 possessed R2=0.863 and Q2=0.799 for training set, and R2=0.710 for test set, while 5-variable model predicting (BOD5/COD)1/2 possessed R2=0.886 and Q2=0.788 for training set, and R2=0.564 for test set. The selected models are interpretable and transparent, reflecting key structural features that influence targeted biodegradability and can be correlated with the degradation mechanisms of studied compounds by UV-C/H2O2.
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