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通过量子力学推导的定量结构-性质关系估算磷脂酰胆碱的链熔化温度

Chain melting temperature estimation for phosphatidyl cholines by quantum mechanically derived quantitative structure property relationships.

作者信息

Holder Andrew J, Yourtee David M, White Derek A, Glaros Alan G, Smith Robert

机构信息

University of Missouri-Kansas City, Department of Chemistry, Kansas City, MO 64110, USA.

出版信息

J Comput Aided Mol Des. 2003 Feb-Apr;17(2-4):223-30. doi: 10.1023/a:1025382226037.

Abstract

Geometries for 62 phosphatidylcholines (PC) were optimized using the AM1 semiempirical quantum mechanical method. Results obtained from these calculations were used to calculate 463 descriptors for each molecule. Quantitative Structure Property Relationships (QSPR) were developed from these descriptors to predict chain melting temperatures (Tm) for the 41 PCs in the training set. After screening each QSPR for statistical validity, the Tm values predicted by each statistically valid QSPR were compared to corresponding Tm values extracted from the literature. The most predictive, chemically meaningful QSPR provided Tm values which agreed with literature values to within experimental error. This QSPR was used to predict Tm values for the remaining 21 PCs to provide external validation for the model. These values also agreed with literature values to within experimental error. The descriptor developed by the final QSPR was the second order average information content, a topological information-theoretical descriptor.

摘要

使用AM1半经验量子力学方法对62种磷脂酰胆碱(PC)的几何结构进行了优化。从这些计算中获得的结果用于为每个分子计算463个描述符。基于这些描述符建立了定量结构-性质关系(QSPR),以预测训练集中41种PC的链熔化温度(Tm)。在对每个QSPR进行统计有效性筛选后,将每个具有统计有效性的QSPR预测的Tm值与从文献中提取的相应Tm值进行比较。预测性最强、化学意义最明确的QSPR给出的Tm值与文献值在实验误差范围内一致。该QSPR用于预测其余21种PC的Tm值,为模型提供外部验证。这些值与文献值在实验误差范围内也一致。最终QSPR所开发的描述符是二阶平均信息含量,这是一种拓扑信息理论描述符。

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