Department of Computer Sciences, Faculty of Informatics, Camaguey University, Camaguey City, 74650, Camaguey, Cuba; Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy. Universidad Central "Martha Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
Departamento de Química, Universidade Federal de Lavras, CP 3037, 37200-000, Lavras, MG, Brazil.
Environ Toxicol Pharmacol. 2017 Dec;56:314-321. doi: 10.1016/j.etap.2017.10.006. Epub 2017 Oct 13.
Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity of set of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log(IGC50)). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure-toxicity relationships; the best model shows values of Q=0.830 and R=0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that these descriptors provide an effective alternative for determining aquatic toxicity of benzene derivatives.
在预测 392 种苯衍生物对原生动物草履虫（log(IGC50)）的水生毒性的一组有机化合物时，使用了来自原子加权向量的几个描述符。这些描述符是使用 MD-LOVIs 软件计算的，并使用各种聚合运算符进行了检查，目的是比较它们在预测水生毒性方面的性能。通过基于信息论的算法，使用可变性分析来量化这些分子描述符的信息量。使用遗传算法的多元线性回归用于获得结构-毒性关系的模型；最佳模型使用六个变量显示 Q=0.830 和 R=0.837 的值。我们的模型与使用相同数据集的其他先前发布的模型相比表现出色。所得结果表明，这些描述符为确定苯衍生物的水生毒性提供了一种有效替代方法。
