Prediction and application in QSPR of aqueous solubility of sulfur-containing aromatic esters using GA-based MLR with quantum descriptors.

Quantitative structure-property relationships (QSPR) were developed using a genetic algorithm (GA)-based variable-selection approach with quantum chemical descriptors derived from AM1-based calculations (MOPAC7.0). With the QSPR models, the aqueous solubility of 71 aromatic sulfur-containing carboxylates, including phenylthio, and phenylsulfonyl carboxylates were efficiently estimated and predicted. Using GA-based multivariate linear regression (MLR) with cross-validation procedure, the most important descriptors were selected from a pool of 28 quantum chemical semi-empirical descriptors, including steric and electronic types, to build QSPR models. The molecular descriptors included molecular surface (SA), charges on carboxyl group (Q(oc)), the magnitude of the difference between E(HOMO) of the solute and ELUMO of water, divided by 100 (E(B)), which were main factors affecting the aqueous solubility of the compounds of interest. The resulted coefficients R and R2 of 0.9571 and 0.9161 and the prediction residual error sum of squares (PRESS) of 13.1768, revealed that it was accurate and reliable for the model to predict the aqueous solubility of the investigated organic compounds. If two outliers were omitted from the dataset, the resulted coefficients R = 0.9619, R2 = 0.9253, and PRESS = 10.3875 were significantly improved. Compared with stepwise regression analysis, the results obtained in this work were better and more reasonable. The best QSPR model were obtained by GA-based MLR. Reasonable mechanisms for aqueous solubility of the sulfur-containing carboxylates were investigated and interpreted.

[1]  Sac-fry Stages,et al.  OECD GUIDELINE FOR TESTING OF CHEMICALS , 2002 .

[2]  S. Baj,et al.  Correlation Between the Chemical Structures of Dialkyl Peroxides and Their Retention in Reversed-Phase High-Performance Liquid Chromatography , 1994 .

[3]  Michele M. Miller,et al.  Relationships between octanol-water partition coefficient and aqueous solubility. , 1985, Environmental science & technology.

[4]  Sujit Banerjee,et al.  Aqueous solubility : methods of estimation for organic compounds , 1992 .

[5]  S. Rault,et al.  Prediction of the fish acute toxicity from heterogeneous data coming from notification files. , 1999, Chemosphere.

[6]  J. Devillers.  Genetic algorithms in molecular modeling , 1996 .

[7]  G. S. Patil.  Prediction of aqueous solubility and octanol—water partition coefficient for pesticides based on their molecular structure , 1994 .

[8]  T. Nevalainen,et al.  New qsar models for polyhalogenated aromatics , 1994 .

[9]  H. B. Krop,et al.  n-Octanol-water partition coefficients, aqueous solubilities and Henry's law constants of fatty acid esters , 1997 .

[10]  Jon W. Ball,et al.  Quantitative structure‐activity relationships for toxicity of phenols using regression analysis and computational neural networks , 1994 .

[11]  C. Hansch,et al.  Linear free-energy relationship between partition coefficients and the aqueous solubility of organic liquids , 1968 .

[12]  Sujit Banerjee,et al.  Water solubility and octanol/water partition coefficients of organics. Limitations of the solubility-partition coefficient correlation , 1980 .

[13]  G. Veith,et al.  QSARs for photoinduced toxicity: I. Acute lethality of polycyclic aromatic hydrocarbons to Daphnia magna , 1994 .

[14]  M. Karelson,et al.  Structurally diverse quantitative structure--property relationship correlations of technologically relevant physical properties , 2000, Journal of chemical information and computer sciences.

[15]  P. Isnard,et al.  Aqueous solubility and n-octanol/water partition coefficient correlations , 1989 .

[16]  Peter C. Jurs,et al.  Prediction of Aqueous Solubility of Organic Compounds from Molecular Structure , 1998, J. Chem. Inf. Comput. Sci..

[17]  Samuel H. Yalkowsky,et al.  Relationships between aqueous solubility and octanol-water partition coefficients , 1980 .

[18]  Z. Zhang,et al.  Prediction of partition coefficient and toxicity for phenylthio, phenylsulfinyl and phenylsulfonyl acetates. , 1995, Environmental science & technology.

[19]  Scott W. Huffman,et al.  Using limited concentration data for the determination of rate constants with the Genetic Algorithm , 1998 .

[20]  Determination and estimation of partitioning properties for phenylthio-carboxylates , 1996 .

[21]  Lian-Sheng Wang,et al.  Determination and estimation of physicochemical properties for phenylsulfonyl acetates , 1995 .

[22]  Paola Gramatica,et al.  QSAR study on the tropospheric degradation of organic compounds , 1999 .

[23]  D. Lewis,et al.  The calculation of molar polarizabilities by the CNDO/2 method: Correlation with the hydrophobic parameter, log P , 1989 .