Development of a New Decision Tree to Rapidly Screen Chemical Estrogenic Activities of Xenopus laevis. Issue 2 (2nd February 2014)

• Record Type: Journal Article
• Authors: Wang, Ting; Li, Weiying; Zheng, Xiaofeng; Lin, Zhifen; Kong, Deyang
• Is Part Of: Molecular informatics. Volume 33:Issue 2(2014:Feb.)
• Page Start: 115
• Page End: 123
• Publication Date: 2014-02-02
• Subjects: Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201300113
• Languages: English
• ISSNs: 1868-1743

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>During the last past decades, there is an increasing number of studies about estrogenic activities of the environmental pollutants on amphibians and many determination methods have been proposed. However, these determination methods are time‐consuming and expensive, and a rapid and simple method to screen and test the chemicals for estrogenic activities to amphibians is therefore imperative. Herein is proposed a new decision tree formulated not only with physicochemical parameters but also a biological parameter that was successfully used to screen estrogenic activities of the chemicals on amphibians. The biological parameter, CDOCKER interaction energy (<italic>E</italic><sub>binding</sub>) between chemicals and the target proteins was calculated based on the method of molecular docking, and it was used to revise the decision tree formulated by Hong only with physicochemical parameters for screening estrogenic activity of chemicals in rat. According to the correlation between <italic>E</italic><sub>binding</sub> of rat and <italic>Xenopus laevis</italic>, a new decision tree for estrogenic activities in <italic>Xenopus laevis</italic> is finally proposed. Then it was validated by using the randomly 8 chemicals which can be frequently exposed to <italic>Xenopus laevis</italic>, and the agreement between the results from the new decision tree and the ones from experiments is generally satisfactory. Consequently, the new decision tree can be used to screen the estrogenic activities of the chemicals, and combinational use of the <italic>E</italic><sub>binding</sub> and classical physicochemical parameters can greatly improves Hong's decision tree.</p> </abstract>