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QSTR 建模以寻找与氨基甲酸酯类毒性相关的 DFT 描述符。

QSTR Modeling to Find Relevant DFT Descriptors Related to the Toxicity of Carbamates.

机构信息

Área Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, Mineral de la Reforma 42184, Mexico.

Escuela Superior de Cómputo, Instituto Politécnico Nacional, Yautepec 62739, Mexico.

出版信息

Molecules. 2022 Aug 28;27(17):5530. doi: 10.3390/molecules27175530.

Abstract

Compounds containing carbamate moieties and their derivatives can generate serious public health threats and environmental problems due their high potential toxicity. In this study, a quantitative structure-toxicity relationship (QSTR) model has been developed by using one hundred seventy-eight carbamate derivatives whose toxicities in rats (oral administration) have been evaluated. The QSRT model was rigorously validated by using either tested or untested compounds falling within the applicability domain of the model. A structure-based evaluation by docking from a series of carbamates with acetylcholinesterase (AChE) was carried out. The toxicity of carbamates was predicted using physicochemical, structural, and quantum molecular descriptors employing a DFT approach. A statistical treatment was developed; the QSRT model showed a determination coefficient () and a leave-one-out coefficient () of 0.6584 and 0.6289, respectively.

摘要

含氨基甲酸酯部分及其衍生物的化合物由于其高潜在毒性,可能会对公共健康和环境造成严重威胁。在这项研究中,建立了一个定量构效关系(QSRT)模型,该模型使用了 178 种氨基甲酸酯衍生物,其在大鼠(口服)中的毒性已经过评估。通过使用属于模型适用性范围的测试或未测试化合物,对 QSRT 模型进行了严格验证。通过与乙酰胆碱酯酶(AChE)进行一系列氨基甲酸酯的对接,进行基于结构的评估。使用基于密度泛函理论(DFT)的物理化学、结构和量子分子描述符来预测氨基甲酸酯的毒性。开发了一种统计处理方法;QSRT 模型的确定系数()和留一法系数()分别为 0.6584 和 0.6289。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2db9/9457808/27445c97636e/molecules-27-05530-g001.jpg

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