Author(s): Norouzi S; Nahmiach N; Perez G; Zhu Y; Peslherbe GH; Muir DCG; Zhang X;

To explore the use of molecular docking as a high throughput in silico screening tool for identifying chemicals of environmental health concern, we conducted a case study to assess endocrine disruption effects due to targeting of nuclear receptors (NRs) by chemicals with backbone structures like bisphenols, but with varied functional groups. The molecular ...

Article GUID: 40970403

Author(s): Zhu FJ; Lu XM; Jia JW; Zhang X; Xing DF; Cai MH; Kallenborn R; Li YF; Muir DCG; Zhang ZF; Zhang X;

Spatial variations of atmospheric alkylated polycyclic aromatic hydrocarbons (Alk-PAHs) are key to understanding their long-range atmospheric transport (LRAT). However, limited Alk-PAHs data have hindered their LRAT characterizations on a global scale. In this study, 49 Alk-PAHs were measured in ...

Article GUID: 40025703

Author(s): Zhang X; Sun X; Jiang R; Zeng EY; Sunderland EM; Muir DCG;

No abstract

Article GUID: 34694120

Author(s): Sun X; Zhang X; Wang L; Li Y; Muir DCG; Zeng EY;

Deep convolutional neural network (DCNN) has proved to be a promising tool for identifying organic chemicals of environmental concern. However, the uncertainty associated with DCNN predictions remains to be quantified. The training process contains many random configurations, including dataset segmentation, input sequences, and initial weight, etc. Moreov ...

Article GUID: 34388923