Xia Binbin, Liu Kunping, Gong Zhiguo, Zheng Bo, Zhang Xiaoyun, Fan Botao
Department of Chemistry, Lanzhou University, Lanzhou 730000, Gansu, PR China.
Ecotoxicol Environ Saf. 2009 Mar;72(3):787-94. doi: 10.1016/j.ecoenv.2008.09.002. Epub 2008 Oct 23.
This paper presents the results of an optimization study on the toxicity of 91 aliphatic and aromatic compounds as well as a small subset of pesticides to algae Chlorella vulgaris, which was accomplished by using quantitative structure-activity relationships (QSAR). The linear (HM) and the nonlinear method radial basis function neural networks (RBFNN) were used to develop the QSAR models and both of them can give satisfactory prediction results. At the same time, by interpreting the descriptors, we can get some insight into structural features (molecular surface area, electrostatic repulsion, and hydrogen bonds) related to the toxic action. Finally, a detailed analysis on the model application domain defined the compounds, whose estimation can be accepted with confidence. The results of this study suggest that the proposed approaches could be successfully used as a general tool for the estimate of novel toxic compounds.
本文介绍了一项关于91种脂肪族和芳香族化合物以及一小部分农药对普通小球藻毒性的优化研究结果，该研究通过定量构效关系（QSAR）完成。采用线性（HM）和非线性方法径向基函数神经网络（RBFNN）建立QSAR模型，二者均能给出令人满意的预测结果。同时，通过解释描述符，我们可以深入了解与毒性作用相关的结构特征（分子表面积、静电排斥和氢键）。最后，对模型应用域进行详细分析，确定了其估计结果可被可靠接受的化合物。本研究结果表明，所提出的方法可成功用作估计新型有毒化合物的通用工具。
