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Conformation-independent quantitative structure-property relationships study on water solubility of pesticides

机译:农药水溶性的构象无关定量结构-性质关系研究

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Water solubility is a key physicochemical parameter in pesticide control and regulation, although sometimes its experimental determination is not an easy task. In this study, we present Quantitative Structure-Property Relationships (QSPRs) for predicting the water solubility at 20 degrees C of 1211 approved heterogeneous pesticide compounds, collected from the online Pesticides Properties Data Base (PPDB). Validated and generally applicable Multivariable Linear Regression (MLR) models were established, including molecular descriptors carrying constitutional and topological aspects of the analyzed compounds. The most representative descriptors were selected from the exploration of a large number of about 18,000 structural variables. A hybrid approach that involves a molecular descriptor, a fingerprint, and a flexible descriptor showed the best predictive performance.
机译:水溶性是农药控制和调节中的关键物理化学参数,尽管有时其实验确定并非易事。在这项研究中,我们提出了定量结构-性质关系(QSPR),用于预测从在线农药特性数据库(PPDB)收集的1211个批准的异质农药化合物在20摄氏度时的水溶性。建立了经过验证的且普遍适用的多变量线性回归(MLR)模型,其中包括带有所分析化合物的组成和拓扑特征的分子描述符。最具代表性的描述符是从对大约18,000个结构变量的大量探索中选择的。包含分子描述符,指纹和灵活描述符的混合方法显示出最佳的预测性能。

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