Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression

• Published in: Environmental toxicology and chemistry Vol. 33; no. 12; pp. 2688 - 2693
• Main Authors: Chen, Guangchao, Li, Xuehua, Chen, Jingwen, Zhang, Ya-nan, Peijnenburg, Willie J.G.M.
• Format: Journal Article
• Language: English
• Published: Pensacola, FL Blackwell Publishing Ltd 01.12.2014; SETAC
• Subjects: Algorithms; Applied sciences; Biodegradability; Biodegradation; Biodegradation, Environmental; Chemicals; Chemistry; Comparative studies; Decision Trees; Dissipation factor; Exact sciences and technology; Functional trees; Global environmental pollution; In silico models; Logistic Models; Logistic regression; Logistics; Mathematical models; Models, Theoretical; Organic chemicals; Organic Chemicals - chemistry; Organic Chemicals - metabolism; Organic chemistry; Organic compounds; Pollution; Predictions; Regression; Regression analysis; Regression models; Test sets; Trees; Logistic regression; Biodegradability; Pollutant behavior; Molecular model; Prediction; In silico models; Decision trees; Functional trees; Decision tree; Modeling; Comparative study; Organic compounds
• ISSN: 0730-7268; 1552-8618
• DOI: 10.1002/etc.2746

Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. Environ Toxicol Chem 2014;33:2688–2693. © 2014 SETAC