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Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping

机译:使用计算机软件预测药物和环境污染物的牛奶/血浆浓度比:基于分类和回归的QSAR和药效团图

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A large set of 185 compounds with diverse molecular structures and different mechanisms of therapeutic actions was used to develop and validate statistically significant classification and regression based QSTR models for predicting partitioning of drugs/chemicals into breast milk. Pharmacophore mapping was also carried out which showed four important features required for lower risk of secretion into milk: (i) hydrophobic group (HYD), (ii) ring aromatic group (RA), (iii) negative ionizable (Neglon) and (iv) hydrogen bond donor (HBA). QSTR and pharmacophore models were rigorously validated internally as well as externally to check the possibilities of any chance correlation and judge the predictive potential of the models. Pharmacological distribution diagrams (PDDs) were used for the classification model as a visualizing technique for the identification and selection of chemicals with lower partitioning into milk. Our in silico models enable to identify the essential structural attributes and quantify the prime molecular pre-requisites which were chiefly responsible for secretion into milk. The developed models were also implemented to screen milk/plasma partitioning potential for a huge number DrugBank database (http://www.drugbank.ca/) compounds.
机译:大量具有不同分子结构和不同治疗机制的185种化合物用于开发和验证基于统计学意义的分类和回归的QSTR模型,以预测药物/化学物质在母乳中的分配。还进行了药理学定位,显示出降低分泌到乳汁中的风险所需的四个重要特征:(i)疏水基团(HYD),(ii)芳香环基团(RA),(iii)负离子化(Neglon)和(iv) )氢键供体(HBA)。 QSTR和药效团模型在内部和外部均经过严格验证,以检查任何机会相关性的可能性并判断模型的预测潜力。药理分布图(PDD)用于分类模型,是一种可视化技术,用于识别和选择分配到牛奶中的化学物质含量较低的化学物质。我们的计算机模拟模型能够识别基本的结构属性,并量化主要分泌到牛奶中的主要分子先决条件。还开发了开发的模型,用于筛选大量DrugBank数据库(http://www.drugbank.ca/)化合物的牛奶/血浆分配潜力。

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