首页> 外文期刊>QSAR & combinatorial science >QSPR study on the melting points of a diverse set of potential ionic liquids by projection pursuit regression
【24h】

QSPR study on the melting points of a diverse set of potential ionic liquids by projection pursuit regression

机译:通过投影寻踪回归对各种潜在离子液体的熔点进行QSPR研究

获取原文
获取原文并翻译 | 示例
       

摘要

A Quantitative Structure - Property Relationship (QSPR) study was carried out to model the melting points for a diverse set of 288 potential Ionic Liquids (ILs) including pyridinium bromides, imidazolium bromides, benzimidazolium bromides, and 1-substituted 4-amino-1,2,4-triazolium bromides. Based on the calculated descriptors by CODESSA program, a Principal Component Analysis (PCA) was performed on the whole data to detect the homogeneities in the dataset and to assist the separation of the data into representative training and test sets. Heuristic Method (HM) and Projection Pursuit Regression (PPR) were used to develop linear and nonlinear models between the descriptors and the melting points. The PPR model gave a high predictive correlation coefficient (R 2) of 0.810 and an Average of Absolute Relative Deviation (AARD) of 17.75%, which are better than those by HM model (R 2=0.712, AARD=24.33%) indicating that PPR is better for the prediction of the melting points. In addition, the descriptors selected by HM can give some insight into factors that can affect the melting points, i.e., benzene ring structure, rotatable bonds, branching, symmetry, and intramolecular electronic effects. This information would be very useful in the design of the potential ILs with desired melting points.
机译:进行了定量结构-性质关系(QSPR)研究,以模拟288种潜在离子液体(IL)的熔点,其中包括吡啶鎓溴化物,咪唑鎓溴化物,苯并咪唑鎓溴化物和1-取代的4-氨基-1, 2,4-三唑溴化物。基于CODESSA程序计算出的描述符,对整个数据执行主成分分析(PCA),以检测数据集中的同质性,并协助将数据分离为代表性的训练集和测试集。启发式方法(HM)和投影寻踪回归(PPR)用于建立描述子和熔点之间的线性和非线性模型。 PPR模型的预测相关系数(R 2)为0.810,平均绝对偏差(AARD)为17.75%,优于HM模型的预测相关系数(R 2 = 0.712,AARD = 24.33%),表明PPR更适合预测熔点。另外,由HM选择的描述符可以对影响熔点的因素提供一些见识,即苯环结构,可旋转键,支化,对称性和分子内电子效应。该信息在设计具有所需熔点的潜在IL时将非常有用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号