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

• Published in: Environmental science & technology Vol. 57; no. 46; pp. 18013 - 18025
• Main Authors: Yu, Zhuohang, Wu, Zengrui, Zhou, Moran, Cao, Kangjia, Li, Weihua, Liu, Guixia, Tang, Yun
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 21.11.2023
• Subjects: Case studies; Chemical fingerprinting; Computer networks; Disruption; Endocrine disruptors; Endocrine Disruptors - chemistry; Endocrine Disruptors - toxicity; Health risks; Humans; Machine learning; Molecular modelling; Nuclear receptors; Nuclear safety; Pharmacology; Pharmacovigilance; Predictions; toxicological profiles; endocrine-disrupting chemicals; network-based inference; pharmacological profiles; computational prediction
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/acs.est.2c08558

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.