文献类型：期刊文献

英文题名：EDC-Predictor: A Novel Strategy for Prediction of Endocrine- Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

作者：Yu, Zhuohang[1];Wu, Zengrui[1];Zhou, Moran[1];Cao, Kangjia[1];Li, Weihua[1];Liu, Guixia[1];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, Shanghai 200237, Peoples R China

年份：2023

卷号：57

期号：46

起止页码：18013

外文期刊名：ENVIRONMENTAL SCIENCE & TECHNOLOGY

收录：;EI(收录号：20231814040152);WOS:【SCI-EXPANDED(收录号:WOS:000970866600001)】；

基金：? ACKNOWLEDGMENTS This work was supported by the National Key Research and Development Program of China (Grant 2019YFA0904800) , the National Natural Science Foundation of China (Grants 82173746 and 82104066) , and the Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission, Grant 2021 Sci & Tech 03-28) .

语种：英文

外文关键词：endocrine-disrupting chemicals; network-based inference; pharmacological profiles; toxicological profiles; computational prediction

摘要：Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.