| Autor: |
Ruiqiu Zhang, Bailun Wang, Ling Li, Shengjie Li, Huizhu Guo, Pei Zhang, Yuqing Hua, Xueyan Cui, Yan Li, Yan Mu, Xin Huang, Xiao Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Ecotoxicology and Environmental Safety, Vol 263, Iss , Pp 115251- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
0147-6513 |
| DOI: |
10.1016/j.ecoenv.2023.115251 |
| Popis: |
Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the past few decades, but the reported models are not always easily available, and few studies have investigated the structural characteristics of EDCs. In the present study, we have developed a series of artificial intelligence models targeting EDC receptors: the androgen receptor (AR); estrogen receptor (ER); and pregnane X receptor (PXR). The consensus models achieved good predictive results for validation sets with balanced accuracy values of 87.37%, 90.13%, and 79.21% for AR, ER, and PXR binding assays, respectively. Analysis of the physical-chemical properties suggested that several chemical properties were significantly (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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