Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India.
SAR QSAR Environ Res. 2009 Jul;20(5-6):551-66. doi: 10.1080/10629360903278735.
Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.
定量构效关系(QSAR)分析分别在属于两组杀虫剂的数据集中进行,即有机磷化合物和氨基甲酸酯。使用了多种类型的描述符,包括拓扑、空间、热力学、信息含量、先导相似性和 E 态指数,以得出杀虫剂活性与化学品结构特性之间的定量关系。基于缺失值、零值、简单相关和多重共线性测试以及遗传算法的系统搜索方法允许对用于生成模型的描述符进行最佳选择。为有机磷化合物和氨基甲酸酯两组开发的 QSAR 模型都具有良好的可预测性,r²值分别为 0.949 和 0.838,以及[图像省略]值分别为 0.890 和 0.765。此外,对于测试集数据,观察到预测和实验 LD(50)值之间存在线性相关性,r²值分别为 0.871 和 0.788,这表明 QSAR 模型的预测准确性是可以接受的。该模型还通过外部验证标准成功进行了测试。本研究中开发的 QSAR 模型应该有助于进一步设计新型有效杀虫剂。