Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113 y 64, C.C. 16, Sucursal 4, 1900, La Plata, Argentina.
Departamento de Química, Facultad de Ciencias Exactas, CONICET, UNLP, Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. J.J. Ronco" (CINDECA), Calle 47 No. 257, B1900AJK, La Plata, Argentina.
Environ Sci Pollut Res Int. 2020 Feb;27(6):6205-6214. doi: 10.1007/s11356-019-06630-9. Epub 2019 Dec 21.
A set of 263 plant-derived compounds with larvicidal activity against Aedes aegypti L. (Diptera: Culicidae) vector is collected from the literature, and is studied by means of a non-conformational quantitative structure-activity relationships (QSAR) approach. The balanced subsets method (BSM) is employed to split the complete dataset into training, validation and test sets. From 26,775 freely available molecular descriptors, the most relevant structural features of compounds affecting the bioactivity are taken. The molecular descriptors are calculated through four different freewares, such as PaDEL, Mold, EPI Suite and QuBiLs-MAS. The replacement method (RM) variable subset selection technique leads to the best linear regression models. A successful QSAR equation involves 7-conformation-independent molecular descriptors, fulfiling the evaluated internal (loo, l30%o, VIF and Y-randomization) and external (test set with N = 65 compounds) validation criteria. The practical application of this QSAR model reveals promising predicted values for some natural compounds with unknown experimental larvicidal activity. Therefore, the present model constitutes the first one based on a large molecular set, being a useful computational tool for identifying and guiding the synthesis of new active molecules inspired by natural products.
从文献中收集了 263 种具有杀蚊幼虫活性的植物衍生化合物，用于对抗埃及伊蚊（双翅目：库蚊科）媒介。采用非构象定量构效关系（QSAR）方法对其进行研究。平衡子集方法（BSM）用于将完整数据集分为训练集、验证集和测试集。从 26775 个免费提供的分子描述符中，选择了影响生物活性的化合物的最相关结构特征。分子描述符通过 PaDEL、Mold、EPI Suite 和 QuBiLs-MAS 等四个不同的免费软件进行计算。替换方法（RM）变量子集选择技术导致最佳线性回归模型。成功的 QSAR 方程涉及 7 个构象独立的分子描述符，满足评估的内部（loo、l30%o、VIF 和 Y-随机化）和外部（测试集，N=65 种化合物）验证标准。该 QSAR 模型的实际应用揭示了一些具有未知实验杀蚊幼虫活性的天然化合物有希望的预测值。因此，该模型是基于大型分子集建立的第一个模型，是一种有用的计算工具，可用于识别和指导受天然产物启发的新活性分子的合成。
