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Free and Open Source Chemistry Software in Research of Quantitative Structure-Toxicity Relationship of Pesticides

机译:杀虫剂定量结构毒性关系研究的自由和开源化学软件

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Pesticides are toxic chemicals aimed for the destroying pest on crops. Numerous data evidence about toxicity of pesticides on aquatic organisms. Since pesticides with similar properties tend to have similar biological activities, toxicity may be predicted from structure. Their structure feature and properties are encoded my means of molecular descriptors. Molecular descriptors can capture quite simple two-dimensional (2D) chemical structures to highly complex three-dimensional (3D) chemical structures. Quantitative structure-toxicity relationship (QSTR) method uses linear regression analyses for correlation toxicity of chemical with their structural feature using molecular descriptors. Molecular descriptors were calculated using open source software PaDEL and in-house built PyMOL plugin (PyDescriptor). PyDescriptor is a new script implemented with the commonly used visualization software PyMOL for calculation of a large and diverse set of easily interpretable molecular descriptors encoding pharmacophoric patterns and atomic fragments. PyDescriptor has several advantages like free and open source, can work on all major platforms (Windows, Linux, MacOS). QSTR method allows prediction of toxicity of pesticides without experimental assay. In the present work, QSTR analysis for toxicity of a dataset of mixtures of 5 classes of pesticides comprising has been performed.
机译:杀虫剂是有毒化学品,旨在破坏农作物的害虫。关于农药毒性对水生生物的众多数据证据。由于具有相似性质的农药倾向于具有相似的生物活性,因此可以从结构中预测毒性。它们的结构特征和属性被编码了我的分子描述符的方法。分子描述符可以捕获相当简单的二维(2D)化学结构,以高度复杂的三维(3D)化学结构。定量结构毒性关系(QSTR)方法使用线性回归分析来使用分子描述夹的化学物质的相关毒性。使用开源软件平板和内部构建的Pymol插件(PyDescriptor)计算分子描述符。 PyDescriptor是一种用常用的可视化软件聚类来实现的新脚本,用于计算编码药物图案和原子片段的大型和多样的易于解释的分子描述符。 PyDescriptor具有免费和开源等优点,可以在所有主要平台上工作(Windows,Linux,MacOS)。 QSTR方法允许在没有实验测定的情况下预测杀虫剂的毒性。在本作的工作中,QSTR分析用于5类群体的混合物数据集的毒性。

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