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化工进展  2015 

定量结构-性质关系在化合物溶解度预测中的研究进展

DOI: 10.16085/j.issn.1000-6613.2015.05.005, PP. 1215-1219

Keywords: 溶解度,定量结构-性质关系,建模,预测

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Abstract:

溶解度是物质十分重要的一种理化性质,其在化工过程、药物和环境等领域的重要性不可忽视.定量结构-性质关系(quantitativestructure-propertyrelationship,QSPR)在化合物溶解度预测中得到广泛的应用.本文介绍了QSPR方法建立溶解度预测模型的研究进展,在总结各类分子描述符和构建溶解度预测模型方法的基础上,分别归纳出三类分子描述符(组成描述符、试验参数及理论计算描述符)和建模方法(线性、非线性及两者联合法),并从不同角度分析它们各自所拥有的特点,比较三类建模的优缺点.最后论述了当前溶解度QSPR研究中存在的不足及未来溶解度预测模型的发展趋势,指出溶解度的预测模型精度有待进一步提高,今后应更关注对化合物在不同pH值、温度、溶剂等更复杂情况下的溶解度预测.

References

[1]  Amidon G,Yalkowsky S,Anik S,et al. Solubility of nonelectrolytes in polar solvents. V. Estimation of the solubility of aliphatic monofunctional compounds in water using a molecular surface area approach[J]. The Journal of Physical Chemistry,1975,79(21):2239-2246.
[2]  van Krevelen D W. Properties of Polymers:Their Estimation and Correlation with Chemical Structure[M]. Amsterdam:Elsevier Scientific Publ.,1976.
[3]  Bicerano J. Prediction of Polymer Properties[M]. New York:Marcel Dekker,1993.
[4]  Ebube N K,Owusu-Ababio G,Adeyeye C M. Preformulation studies and characterization of the physicochemical properties of amorphous polymers using artificial neural networks[J]. International Journal of Pharmaceutics,2000,196(1):27-35.
[5]  任伟,孔德信. 定量构效关系研究中分子描述符的相关性[J]. 计算机与应用化学,2009(11):1455-1458.
[6]  Wang J,Hou T. Recent advances on aqueous solubility prediction[J]. Combinatorial Chemistry & High Throughput Screening,2011,14(5):328-338.
[7]  Klopman G,Zhu H. Estimation of the aqueous solubility of organic molecules by the group contribution approach[J]. Journal of Chemical Information and Computer Sciences,2001,41(2):439-445.
[8]  Jain N,Yalkowsky S H. Estimation of the aqueous solubility Ⅰ:Application to organic nonelectrolytes[J]. Journal of Pharmaceutical Sciences,2001,90(2):234-252. 3.0.CO;2-V target="_blank">
[9]  Jouyban A,Shayanfar A,Ghafourian T,et al. Solubility prediction of pharmaceuticals in dioxane+water mixtures at various temperatures:Effects of different descriptors and feature selection methods[J]. Journal of Molecular Liquids,2014,195:125-131.
[10]  Jiao L,Li H. QSPR studies on the aqueous solubility of PCDD/Fs by using artificial neural network combined with stepwise regression[J]. Chemometrics and Intelligent Laboratory Systems,2010,103(2):90-95.
[11]  Leardi R,Boggia R,Terrile M. Genetic algorithms as a strategy for feature selection[J]. Journal of Chemometrics,1992,6(5):267-281.
[12]  Yin C,Liu X,Guo W,et al. Prediction and application in QSPR of aqueous solubility of sulfur-containing aromatic esters using GA-based MLR with quantum descriptors[J]. Water Research,2002,36(12):2975-2982.
[13]  Duchowicz P R,Talevi A,Bruno-Blanch L E,et al. New QSPR study for the prediction of aqueous solubility of drug-like compounds[J]. Bioorganic & Medicinal Chemistry,2008,16(17):7944-7955.
[14]  Noru?is Marija J. SPSS Inc. SPSS Professional Statistics 6.1[M]. Prentice Hall,1994.
[15]  SAS Visual Analytics 6. 1:User's Guide[M]. Sas Institute,2012.
[16]  Guide M U. The Mathworks[M]. Inc.,Natick,MA,1998.
[17]  StatSoft H. Statistica 6[M]. Springer,2002.
[18]  潘善飞,胡桂香,吕杨,等. 离子液体中有机物溶解度的QSPR模型分析[J]. 物理化学学报,2010,26(9):2494-2502.
[19]  王振东,杨锋,周培疆. 分子连接性指数对部分有机污染物溶解度及疏水参数的预测[J]. 环境化学,2003,22(4):380-384.
[20]  Hewitt M,Cronin M T,Enoch S J,et al. In silico prediction of aqueous solubility:The solubility challenge[J]. Journal of Chemical Information and Modeling,2009,49(11):2572-2587.
[21]  Hughes L D,Palmer D S,Nigsch F,et al. Why are some properties more difficult to predict than others? A study of QSPR models of solubility,melting point,and Log P[J]. Journal of Chemical Information and Modeling,2008,48(1):220-232.
[22]  Zhou D,Alelyunas Y,Liu R. Scores of extended connectivity fingerprint as descriptors in QSPR study of melting point and aqueous solubility[J]. Journal of Chemical Information and Modeling,2008,48(5):981-987.
[23]  王洪元,史国栋. 人工神经网络技术及其应用[M]. 北京:中国石化出版社,2002.
[24]  Mehrpooya M,Mohammadi A H,Richon D. Extension of an artificial neural network algorithm for estimating sulfur content of sour gases at elevated temperatures and pressures[J]. Industrial & Engineering Chemistry Research,2009,49(1):439-442.
[25]  Gharagheizi F,Eslamimanesh A,Mohammadi A H,et al. Representation/prediction of solubilities of pure compounds in water using artificial neural network——Group contribution method[J]. Journal of Chemical & Engineering Data,2011,56(4):720-726.
[26]  Wilczyńska Piliszek A J,Piliszek S,Falandysz J. QSAR and ANN for the estimation of water solubility of 209 polychlorinated trans-azobenzenes[J]. Journal of Environmental Science and Health,Part A,2012,47(2):155-166.
[27]  马卫平. 线性和非线性方法在QSAR/QSPR研究中的应用[D]. 兰州:兰州大学,2007.
[28]  Rostami H,Khaksar Manshad A. Prediction of asphaltene precipitation in live and tank crude oil using gaussian process regression[J]. Petroleum Science and Technology,2013,31(9):913-922.
[29]  Gharagheizi F,Alamdari R F. A molecular‐based model for prediction of solubility of C60 fullerene in various solvents[J]. Fullerenes,Nanotubes,and Carbon Nonstructures,2008,16(1):40-57.
[30]  Liu Y,Sun X,Ouyang A. Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN[J]. LWT-Food Science and Technology,2010,43(4):602-607.
[31]  Hansen N T,Kouskoumvekaki I,J?rgensen F S,et al. Prediction of pH-dependent aqueous solubility of druglike molecules[J]. Journal of Chemical Information and Modeling,2006,46(6):2601-2609.
[32]  Hasselbalch K. Calculation of blood pH based on the free and bound carbonic acid,and oxygen binding of blood as function of pH[J]. Die Biochem. Z,1916,78:112-144.
[33]  Wang J,Hou T,Xu X. Aqueous solubility prediction based on weighted atom type counts and solvent accessible surface areas[J]. Journal of Chemical Information and Modeling,2009,49(3):571-581.
[34]  Huuskonen J,Rantanen J,Livingstone D. Prediction of aqueous solubility for a diverse set of organic compounds based on atom-type electrotopological state indices[J]. European Journal of Medicinal Chemistry,2000,35(12):1081-1088.
[35]  Valenzuela L M,Reveco A,del Valle J M. Modelling solubility in supercritical carbon dioxide using quantitative structure-property relationships[J]. The Journal of Supercritical Fluids,2014,94(10):113-122.

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