Quantitative Structure–Activity Relationship Study on Fish Toxicity of Substituted Benzenes
Quantitative Structure–Activity Relationship Study on Fish Toxicity of Substituted Benzenes
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DOI：
10.1002/qsar.200710096
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发表时间：
2008-08
期刊：
影响因子：
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通讯作者：
Zhiguo Gong;B. Xia;Ruisheng Zhang;Xiaoyun Zhang;B. Fan
中科院分区：
文献类型：
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作者：
Zhiguo Gong;B. Xia;Ruisheng Zhang;Xiaoyun Zhang;B. Fan
Many chemicals cause latent harm, such as erratic diseases and change of climate, and therefore it is necessary to evaluate environmentally safe levels of dangerous chemicals. Quantitative Structure–Toxicity Relationship (QSTR) analysis has become an indispensable tool in ecotoxicological risk assessments. Our paper used QSTR to deal with the modeling of the acute toxicity of 92 substituted benzenes. The molecular descriptors representing the structural features of the compounds were calculated by CODESSA program. Heuristic Method (HM) and Radial Basis Function Neural Networks (RBFNNs) were utilized to construct the linear and the nonlinear QSTR models, respectively. The predictive results were in agreement with the experimental values. The optimal QSTR model which was established based on RBFNNs gave a correlation coefficient (R2) of 0.893, 0.876, 0.889 and Root-Mean-Square Error (RMSE) of 0.220, 0.205, 0.218 for the training set, the test set, and the whole set, respectively. RBFNNs proved to be a very good method to assess acute aquatic toxicity of these compounds, and more importantly, the RBFNNs model established in this paper has fewer descriptors and better results than other models reported in previous literatures. The current model allows a more transparent chemical interpretation of the acute toxicity in terms of intermolecular interactions.
