Suppr超能文献

研究芳氧基氨丙醇衍生物与替考拉宁和万古霉素相的物理化学相互作用,以期进行定量构效关系研究。

Study of physicochemical interaction of aryloxyaminopropanol derivatives with teicoplanin and vancomycin phases in view of quantitative structure-property relationship studies.

机构信息

Department of Chemistry, Faculty of Natural Sciences, University of Ss. Cyril and Methodius, Nám. J. Herdu 2, SK-917 01 Trnava, Slovakia.

出版信息

J Chromatogr A. 2013 Aug 2;1301:38-47. doi: 10.1016/j.chroma.2013.05.046. Epub 2013 May 31.

Abstract

The aim of this work was to study the physicochemical interactions between chiral stationary phases and chiral molecules and to elucidate which of the specific interactions are more or less important. The HPLC separation of 58 aryloxyaminopropanols was performed on two chiral stationary phases containing the macrocyclic antibiotics teicoplanin or vancomycin and using a methanol/acetonitrile/acetic acid/triethylamine mobile phase (volume ratios 45/55/0.3/0.2). The resolution of enantiomers (Rij) as the target variable was predicted for the mentioned kind of compounds by means of thoroughly selected descriptors provided by the applied Dragon software. The created QSPR models can be considered as a way to explore and discover new relationships or interactions between the quantitative structure and resolution of enantiomers. For calculation and validation of the QSPR models, different modelling methodologies were applied based on MLR (multiple linear regression) and ANN (artificial neural network) techniques. Both methods exhibit an ability for successful prediction of the enantioresolution characteristics of the studied molecules. The results seem to demonstrate that it is possible to predict resolution values of enantiomeric separations of related compounds on given chromatographic systems.

摘要

本工作旨在研究手性固定相和手性分子之间的物理化学相互作用,并阐明哪些特定相互作用更为重要。在含有大环抗生素替考拉宁或万古霉素的两种手性固定相上,使用甲醇/乙腈/乙酸/三乙胺流动相(体积比 45/55/0.3/0.2),对 58 种芳氧基丙醇进行了 HPLC 分离。通过应用的 Dragon 软件提供的经过精心选择的描述符,以对映体的分离度(Rij)作为目标变量,对上述类型的化合物进行了预测。所创建的 QSPR 模型可以被视为探索和发现定量结构与对映体分离之间新关系或相互作用的一种方法。为了计算和验证 QSPR 模型,基于 MLR(多元线性回归)和 ANN(人工神经网络)技术,应用了不同的建模方法。这两种方法都表现出成功预测研究分子对映体分辨率特征的能力。结果似乎表明,在给定的色谱系统上,有可能预测相关化合物对映体拆分的分辨率值。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验