文献类型：期刊文献

英文题名：In Silico Prediction of Endocrine Disrupting Chemicals Using Single-Label and Multilabel Models

作者：Sun, Lixia[1];Yang, Hongbin[1];Cai, Yingchun[1];Li, Weihua[1];Liu, Guixia[1];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China

年份：2019

卷号：59

期号：3

起止页码：973

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20191206666587);WOS:【SCI-EXPANDED(收录号:WOS:000462943700004)】；

基金：This work was supported by the National Key Research and Development Program (Grant 2016YFA0502304) and the National Natural Science Foundation of China (Grant 81872800).

语种：英文

外文关键词：Cytology - Forecasting - Health risks - Endocrine disrupters - Water pollution - Learning systems

摘要：Endocrine disruption (ED) has become a serious public health issue and also poses a significant threat to the ecosystem. Due to complex mechanisms of ED, traditional in silico models focusing on only one mechanism are insufficient for detection of endocrine disrupting chemicals (EDCs), let alone offering an overview of possible action mechanisms for a known EDC. To remove these limitations, in this study both single-label and multilabel models were constructed across six ED targets, namely, AR (androgen receptor), ER (estrogen receptor alpha), TR (thyroid receptor), GR (glucocorticoid receptor), PPARg (peroxisome proliferator-activated receptor gamma), and aromatase. Two machine learning methods were used to build the single-label models, with multiple random under-sampling combining voting classification to overcome the challenge of data imbalance. Four methods were explored to construct the multilabel models that can predict the interaction of one EDC against multiple targets simultaneously. The single-label models of all the six targets have achieved reasonable performance with balanced accuracy (BA) values from 0.742 to 0.816. Each top single-label model was then joined to predict the multilabel test set with BA values from 0.586 to 0.711. The multilabel models could offer a significant boost over the single-label baselines with BA values for the multilabel test set from 0.659 to 0.832. Therefore, we concluded that single-label models could be employed for identification of potential EDCs, while multilabel ones are preferable for prediction of possible mechanisms of known EDCs.