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Li, F.; Wu, H.; Li, L.; Li, X.; Zhao, J.; Peijnenburg, W. J. G. M. Docking and QSAR study on the binding interactions between polycyclic aromatic hydrocarbons and estrogen receptor. Ecotoxicology and Environmental Safety 2012, 80, 273 - 279.

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Li, F.; Wu, H.; Li, L.; Li, X.; Zhao, J.; Peijnenburg, W. J. G. M. Docking and QSAR study on the binding interactions between polycyclic aromatic hydrocarbons and estrogen receptor. Ecotoxicology and Environmental Safety 2012, 80, 273 - 279.

QDB archive DOI: 10.15152/QDB.187   DOWNLOAD

QsarDB content

Property logRBA: Relative binding affinity to hERa i

Compounds: 36 | Models: 1 | Predictions: 2

PLS: Model for polycyclic aromatic hydrocarbons

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 29 0.941 0.192
Validation set external validation 7 0.749 0.139

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Title: Li, F.; Wu, H.; Li, L.; Li, X.; Zhao, J.; Peijnenburg, W. J. G. M. Docking and QSAR study on the binding interactions between polycyclic aromatic hydrocarbons and estrogen receptor. Ecotoxicology and Environmental Safety 2012, 80, 273 - 279.
Abstract: Little is known about the estrogenic activities of polycyclic aromatic hydrocarbons (PAHs) and the underlying mechanisms on estrogenic activities are still unclear. Molecular docking and quantitative structure–activity relationship (QSAR) were used to understand the relationship between molecular structural features and estrogenic activity, and to predict the binding affinity of PAHs to estrogen receptor α (ERα). From molecular docking analysis, hydrogen bonding as well as hydrophobic and π interactions were found between PAHs and ERα. Based on the docking results, appropriate molecular structural parameters were adopted to develop a QSAR model. Five descriptors were included in the QSAR model, which indicated that the estrogenic activity was related to molecular size, van der Waals volumes, shape profiles, polarizabilities and electropological states were significant parameters explaining the estrogenicity. Comparatively, the developed QSAR model had good robustness, predictive ability and mechanistic interpretability. Moreover, the applicability domain of the model was described.
URI: http://hdl.handle.net/10967/187
http://dx.doi.org/10.15152/QDB.187
Date: 2016-09-27


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