Journal
SCIENTIFIC REPORTS
Volume 7, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41598-017-02365-0
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Funding
- National Natural Science Foundation of China [31570160]
- Innovation Team Project of Education Department of Liaoning Province [LT2015011]
- General Research Project Foundation of Education Department of Liaoning Province [L2014001]
- Large-scale Equipment Shared Services Project of Science and Technology Bureau of Shenyang [F15165400]
- Applied Basic Research Project of Science and Technology Bureau of Shenyang [F16205151]
- Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning
- Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province
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Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 +/- 2.9%, sensitivity of 67.0 +/- 5.0%, and specificity of 73.1 +/- 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models (http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/).
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