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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
QSAR model for predicting the toxicity of organic compounds to fathead minnow
Qingzhu Jia1, Yunpeng Zhao1, Fangyou Yan2
1School of Marine and Environmental Science, Tianjin University of Science and Technology, 13 St. 29, TEDA, 300457, Tianjin, People's Republic of China.
Abstract:
In this work, a new norm descriptor is proposed based on atomic properties. A quantitative structure-activity relationship (QSAR) model for predicting the toxicity of organic compounds to fathead minnow is further developed by norm descriptors. Results indicate that this new model based on the norm descriptors has satisfactory predictive results with the squared correlation coefficient (R2) and squared relation coefficient of the cross validation (Q2) of 0.8174 and 0.7923, respectively. Combining with Y-randomization test, applicability domain test, and comparison with other references, calculation results indicate that the QSAR model performs well both in the stability and the accuracy with wide application domain, which might be further used effectively for the safe and risk assessment of various organics.
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