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Issue 2, 2018
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In silico prediction of chemical genotoxicity using machine learning methods and structural alerts

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Abstract

Genotoxicity tests can detect compounds that have an adverse effect on the process of heredity. The in vivo micronucleus assay, a genotoxicity test method, has been widely used to evaluate the presence and extent of chromosomal damage in human beings. Due to the high cost and laboriousness of experimental tests, computational approaches for predicting genotoxicity based on chemical structures and properties are recognized as an alternative. In this study, a dataset containing 641 diverse chemicals was collected and the molecules were represented by both fingerprints and molecular descriptors. Then classification models were constructed by six machine learning methods, including the support vector machine (SVM), naïve Bayes (NB), k-nearest neighbor (kNN), C4.5 decision tree (DT), random forest (RF) and artificial neural network (ANN). The performance of the models was estimated by five-fold cross-validation and an external validation set. The top ten models showed excellent performance for the external validation with accuracies ranging from 0.846 to 0.938, among which models Pubchem_SVM and MACCS_RF showed a more reliable predictive ability. The applicability domain was also defined to distinguish favorable predictions from unfavorable ones. Finally, ten structural fragments which can be used to assess the genotoxicity potential of a chemical were identified by using information gain and structural fragment frequency analysis. Our models might be helpful for the initial screening of potential genotoxic compounds.

Graphical abstract: In silico prediction of chemical genotoxicity using machine learning methods and structural alerts

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Publication details

The article was received on 29 Sep 2017, accepted on 14 Dec 2017 and first published on 15 Dec 2017


Article type: Paper
DOI: 10.1039/C7TX00259A
Citation: Toxicol. Res., 2018,7, 211-220
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    In silico prediction of chemical genotoxicity using machine learning methods and structural alerts

    D. Fan, H. Yang, F. Li, L. Sun, P. Di, W. Li, Y. Tang and G. Liu, Toxicol. Res., 2018, 7, 211
    DOI: 10.1039/C7TX00259A

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