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Molecular docking study to evaluate the carcinogenic potential and mammalian toxicity of thiophosphonate pesticides by cluster and discriminant analysis

机译:分子对接研究,通过聚类和判别分析评估硫代膦酸酯农药的致癌潜力和哺乳动物毒性

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摘要

In this paper, the carcinogenic potential and mammalian toxicity on rodents, based on the quantitative relationship models between structure and biological activity (QSAR), were evaluated. The carcinogenic-ity and acute toxicity were evaluated by docking molecular physicochemical descriptors, on a series of 33 thiophosphonates. These properties, mainly hydrophobicity, electronic distribution, hydrogen bonding characteristics, molecule size and flexibility, and the presence of various pharmacophoric features, influence the behavior of molecule in a living organism, including bioavailability, transport properties, affinity to proteins, reactivity, toxicity, metabolic stability and many others. The model was validated using linear regression methods: principal component analysis (PCA), partial least squares (PLS) and multiple linear regression (MLR); non-linear regression methods: cluster analysis (CA) and discriminant analysis (DA); and neural network analysis: probabilistic neural network (PNN), identifying the best predictor.
机译:本文基于结构与生物活性之间的定量关系模型,对啮齿类动物的致癌潜力和哺乳动物毒性进行了评估。通过对接33种硫代膦酸酯,通过对接分子理化指标来评估其致癌性和急性毒性。这些特性(主要是疏水性,电子分布,氢键特性,分子大小和柔韧性以及各种药效学特性的存在)会影响分子在活生物体中的行为,包括生物利用度,转运特性,对蛋白质的亲和力,反应性,毒性,代谢稳定性等。使用线性回归方法验证了该模型:主成分分析(PCA),偏最小二乘(PLS)和多元线性回归(MLR);非线性回归方法:聚类分析(CA)和判别分析(DA);和神经网络分析:概率神经网络(PNN),确定最佳预测变量。

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