Acta Phys. -Chim. Sin. ›› 2010, Vol. 26 ›› Issue (09): 2494-2502.doi: 10.3866/PKU.WHXB20100902

• QUANTUM CHEMISTRY AND COMPUTATION CHEMISTRY • Previous Articles     Next Articles

QSPRModel Analysis on the Solubility of Organic Compounds in Ionic Liquids

PAN Shan-Fei1,2, HU Gui-Xiang1, LV Yang1,2, ZOU Jian-Wei1, YU Qing-Sen1,2   

  1. 1. Key Laboratory for Molecular Design and Nutrition Engineering of Ningbo City, Ningbo Institute of Technology,Zhejiang University, Ningbo 315100, Zhejiang Province, P. R. China;
    2. Department of Chemistry,Zhejiang University, Hangzhou 310027, P. R. China
  • Received:2010-02-26 Revised:2010-05-11 Published:2010-09-02
  • Contact: HU Gui-Xiang E-mail:hugx@nit.zju.edu.cn
  • Supported by:

    The project was supported by the National Natural Science Foundation of China (20803063).

Abstract:

A quantitative structure-property relationship (QSPR) study on the solubility of 84 organic compounds in 4 different ionic liquids was done based on VolSurf parameters using the partial least square (PLS) statistical method and good results were obtained. The training set model predicts the solubilities of the test set well. An analysis of the VolSurf descriptors show that large volume hydrophilic regions are beneficial for solubility, and the interaction energy is about -0.84 kJ·mol-1 between the organic compounds and the ionic liquids. A certain degree of hydrophobicity is also favorable for solubility. When the ionic liquids have a small hydrophobic substituent, an asymmetric partial hydrophobic region in the organic compound is advantageous for solubility. If the ionic liquid has a large hydrophobic substituent, a large hydrophobic region in the organic compound benefits the solubility. Multiple linear regression (MLR) analysis shows that hydrophilic parameterW1 is the most important parameter, which indicates that hydrophilicity is a key factor that influences the solubility of organic compounds in ionic liquids.

Key words: Ionic liquid, Solubility, Quantitative structure-property relationship, VolSurf, Partial least square, Multiple linear regression