期刊
ENVIRONMENTAL POLLUTION
卷 253, 期 -, 页码 29-38出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.envpol.2019.06.081
关键词
Deep learning (DL); Emerging contaminants (ECs); Endocrine disrupting chemicals (EDCs); Sex hormone binding globulin (SHBG); Estrogen receptor (ER); Quantitative structure-activity relationship (QSAR)
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [NRF-2017R1E1A1A03070713]
- Railway Technology Research Project of the Ministry of Land Infrastructure and Transport [19QPPW-B152307-01]
Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human health. Quantitative structure-activity relationship (QSAR) models are a promising alternative to in vitro methods to predict the toxicological effects of chemicals on human health. In this study, we assessed a deep-learning based QSAR (DL-QSAR) model to predict the qualitative and the quantitative effects of EDCs on the human endocrine system, and especially sex-hormone binding globulin (SHBG) and estrogen receptor (ER). Statistical analyses of the qualitative responses indicated that the accuracies of all three DL-QSAR methods were above 90%, and greater than the other statistical and machine learning models, indicating excellent classification performance. The quantitative analyses, as assessed using deep-neural-network-based QSAR (DNN-QSAR), resulted in a coefficient of determination (R-2) of 0.80 and predictive square correlation coefficient (Q(2)) of 0.86, which implied satisfactory goodness of fit and predictive ability. Thus, DNN was able to transform sparse molecular descriptors into higher dimensional spaces, and was superior for assessment qualitative responses. Moreover, DNN-QSAR demonstrated excellent performance in the discipline of computational chemistry by handling multicollinearity and overfitting problems. (C) 2019 Elsevier Ltd. All rights reserved.
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