Research Group of Environmental Chemistry, Ecotoxicology & Food Toxicology, Institute of Environmental Sciences & Public Health, University of Gdańsk, Gdańsk, Poland.
J Environ Sci Health B. 2012;47(4):275-87. doi: 10.1080/03601234.2012.638885.
The quantitative structure - property relationship (QSPR) and the artificial neural networks (ANNs) methods were used to estimate aqueous solubility (log S and μg/L) of polychlorinated trans-azoxybenzenes (PCt-ABs). These QSPR and ANN models are based on geometry optimalization and quantum-chemical structural descriptors, which were computed on the level of density functional theory (DFT) using B3LYP functional and 6-311++G** basis set in Gaussian 03 software and the semi-empirical quantum chemistry method for property parameterization (RM1) in the molecular orbital package (MOPAC) software. The predicted solubility of PCt-AOBs by RM1 and DFT models and depending on a congener varied within a homologue class between 47-19498 and 371-1738 μg/L for Mono-; 33-11481 and 7.9-3630 μg/L for Di-; 6.1-4786 and 4.7-12882 μg/L for Tri-; 1.3-1174 and 0.3-14791 μg/L for Tetra-; 0.4-646 and 0.1-38904 μg/L for Penta-; 0.1-155 and 0.2-63096 μg/L for Hexa-; 0.2-27 and 0.1-646 μg/L for Hepta-; < 0.1-6.2 and 0.8-282 μg/L for Octa-; 0.6-2.6 and 0.8-12 μg/L for NonaCt-AOBs; and 1.2 and 0.5 μg/L for DecaCt-AOB, respectively. Both computational models used were characterized by good predictive abilities and small errors, while calculations by RM1 method were highly competitive compared to a much more time-consuming and expensive method by DFT.
定量构效关系（QSPR）和人工神经网络（ANNs）方法被用于估计多氯代反式偶氮苯（PCt-ABs）在水中的溶解度（log S 和 μg/L）。这些 QSPR 和 ANN 模型基于几何优化和量子化学结构描述符，这些描述符是在 Gaussian 03 软件中使用 B3LYP 函数和 6-311++G** 基组在密度泛函理论（DFT）水平上计算得到的，而在分子轨道包（MOPAC）软件中使用半经验量子化学方法（RM1）进行属性参数化。RM1 和 DFT 模型预测的 PCt-AOBs 溶解度取决于同系物，在同系物类别内，单取代物的范围为 47-19498 和 371-1738 μg/L；二取代物的范围为 33-11481 和 7.9-3630 μg/L；三取代物的范围为 6.1-4786 和 4.7-12882 μg/L；四取代物的范围为 1.3-1174 和 0.3-14791 μg/L；五取代物的范围为 0.4-646 和 0.1-38904 μg/L；六取代物的范围为 0.1-155 和 0.2-63096 μg/L；七取代物的范围为 0.2-27 和 0.1-646 μg/L；八取代物的范围为 <0.1-6.2 和 0.8-282 μg/L；九取代物的范围为 0.6-2.6 和 0.8-12 μg/L；十取代物的范围为 1.2 和 0.5 μg/L。这两种所使用的计算模型都具有良好的预测能力和较小的误差，而 RM1 方法的计算结果与更耗时、更昂贵的 DFT 方法相比具有很强的竞争力。
