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Korean Chemical Society (대한화학회)
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Prediction Acidity Constant of Various Benzoic Acids and Phenols in Water Using Linear and Nonlinear QSPR Models

  • Published : 2005.12.20
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Abstract

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

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References

  1. Katritzky, A. R.; Karelson, M.; Lobanov, V. S. Pure Appl. Chem. 1997, 69, 245 https://doi.org/10.1351/pac199769020245
  2. Balaban, A. T. J. Chem. Inf. Comut. Sci. 1997, 37, 645 https://doi.org/10.1021/ci960168x
  3. Benfenati, E.; Gini, G. Toxicology 1997, 119, 213 https://doi.org/10.1016/S0300-483X(97)03631-7
  4. Cronce, D. T.; Famini, G. R.; Soto, J. A. D.; Wilson, L. Y. J. Chem. Soc., Perkin Trans. 2 1998, 1293
  5. Engberts, J. B. F. N.; Famini, G. R.; Perjessy, A.; Wilson, L. Y. J. Phys. Org. Chem. 1998, 11, 261 https://doi.org/10.1002/(SICI)1099-1395(199804)11:4<261::AID-POC997>3.0.CO;2-0
  6. Hiob, R.; Karelson, M. J. Chem. Inf. Comut. Sci. 2000, 40, 1062 https://doi.org/10.1021/ci0004457
  7. Habibi-Yangjeh, A. Indian J. Chem. 2003, 42B, 1478
  8. Habibi-Yangjeh, A. Indian J. Chem. 2004, 43B, 1504
  9. Nikolic, S.; Milicevic, A.; Trinajstic, N.; Juric, A. Molecules 2004, 9, 1208 https://doi.org/10.3390/91201208
  10. Devillers, J. SAR and QSAR Environ. Res. 2004, 15, 501 https://doi.org/10.1080/10629360412331297443
  11. Karelson, M.; Lobanov, V. S. Chem. Rev. 1996, 96, 1027 https://doi.org/10.1021/cr950202r
  12. Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; Wiley-VCH: Weinheim, Germany, 2000
  13. Kramer, R. Chemometric Techniques for Quantitative Analysis; Marcel Dekker: New York, 1998
  14. Wold, S.; Sjöström, M. Chemom. Intell. Lab. Syst. 1998, 44, 3 https://doi.org/10.1016/S0169-7439(98)00075-6
  15. Barros, A. S.; Rutledge, D. N. Chemomet. Intell. Lab. Syst. 1998, 40, 65 https://doi.org/10.1016/S0169-7439(98)00002-1
  16. Garkani-Nejad, Z.; Karlovits, M.; Demuth, W.; Stimpfl, T.; Vycudilik, W.; Jalali-Heravi, M.; Varmuza, K. J. Chromatogr. A 2004, 1028, 287 https://doi.org/10.1016/j.chroma.2003.12.003
  17. Patterson, D. W. Artificial Neural Networks: Theory and Applications; Simon and Schuster: New York, 1996; Part III, Ch. 6
  18. Bose, N. K.; Liang, P. Neural Network Fundamentals; McGraw- Hill: New York, 1996
  19. Zupan, J.; Gasteiger, J. Neural Networks in Chemistry and Drug Design; Wiley-VCH: Weinhein, 1999
  20. Agatonovic-Kustrin, S.; Beresford, R. J. Pharm. Biomed. Anal. 2000, 22, 717 https://doi.org/10.1016/S0731-7085(99)00272-1
  21. Fatemi, M. H. J. Chromatogr. A 2002, 955, 273 https://doi.org/10.1016/S0021-9673(02)00169-3
  22. Xing, W. L.; He, X. W. Anal. Chim. Acta 1997, 349, 283 https://doi.org/10.1016/S0003-2670(97)00249-3
  23. Bunz, A. P.; Braun, B.; Janowsky, R. Fluid Phase Equilib. 1999, 158, 367 https://doi.org/10.1016/S0378-3812(99)00058-8
  24. Homer, J.; Generalis, S. C.; Robson, J. H. Phys. Chem. Chem. Phys. 1999, 1, 4075 https://doi.org/10.1039/a904096j
  25. Goll, E. S.; Jurs, P. C. J. Chem. Inf. Comp. Sci. 1999, 39, 974 https://doi.org/10.1021/ci990071l
  26. Vendrame, R.; Braga, R. S.; Takahata, Y.; Galvao, D. S. J. Chem. Inf. Comput. Sci. 1999, 39, 1094 https://doi.org/10.1021/ci990326v
  27. Gaspelin, M.; Tusar, L.; Smid-Korbar, J.; Zupan, J.; Kristl, J. Int. J. Pharm. 2000, 196, 37 https://doi.org/10.1016/S0378-5173(99)00443-3
  28. Gini, G.; Cracium, M. V.; Konig, C.; Benfenati, E. J. Chem. Inf. Comput. Sci. 2004, 44, 1897 https://doi.org/10.1021/ci0401219
  29. Urata, S.; Takada, A.; Uchimaru, T.; Chandra, A. K.; Sekiya, A. J. Fluorine Chem. 2002, 116, 163 https://doi.org/10.1016/S0022-1139(02)00128-8
  30. Koziol, J. Internet Electron J. Mol. Des. 2003, 2, 315
  31. Wegner, J. K.; Zell, A. J. Chem. Inf. Comput. Sci. 2003, 43, 1077 https://doi.org/10.1021/ci034006u
  32. Valkova, I.; Vracko, M.; Basak, S. C. Anal. Chim. Acta 2004, 509, 179 https://doi.org/10.1016/j.aca.2003.12.035
  33. Sebastiao, R. C. O.; Braga, J. P.; Yoshida, M. I. Thermochimica Acta 2004, 412, 107 https://doi.org/10.1016/j.tca.2003.09.009
  34. Jalali-Heravi, M.; Masoum, S.; Shahbazikhah, P. J. Magn. Reson. 2004, 171, 176 https://doi.org/10.1016/j.jmr.2004.08.011
  35. Habibi-Yangjeh, A.; Nooshyar, M. Bull. Korean Chem. Soc. 2005, 26, 139 https://doi.org/10.5012/bkcs.2005.26.1.139
  36. Habibi-Yangjeh, A.; Nooshyar, M. Physics and Chemistry of Liquids 2005, 43, 239 https://doi.org/10.1080/00319100500061233
  37. Selassie, C. D.; DeSoyza, T. V.; Rosario, M.; Gao, H.; Hansch, C. Chemico-Biological Interaction 1998, 113, 175 https://doi.org/10.1016/S0009-2797(98)00027-1
  38. Zhao, Y.-H.; Yuan, L.-H.; Wang, L.-S. Bull. Environ. Contam. Toxicol. 1996, 57, 242 https://doi.org/10.1007/s001289900182
  39. Hemmateenejad, B.; Sharghi, H.; Akhond, M.; Shamsipur, M. J. Solution Chem. 2003, 32, 215 https://doi.org/10.1023/A:1022982200712
  40. Gruber, C.; Buss, V. Chemosphere 1989, 19, 1595 https://doi.org/10.1016/0045-6535(89)90503-1
  41. Citra, M. J. Chemosphere 1999, 38, 191 https://doi.org/10.1016/S0045-6535(98)00172-6
  42. Schuurmann, G. Quant. Struct. Act. Relat. 1996, 15, 121 https://doi.org/10.1002/qsar.19960150206
  43. Gross, K. C.; Seybold, P. G. Int. J. Quant. Chem. 2001, 85, 569 https://doi.org/10.1002/qua.1525
  44. Liptak, M. D.; Gross, K. C.; Seybold, P. G.; Feldgus, S.; Shields, G. C. J. Am. Chem. Soc. 2002, 124, 6421 https://doi.org/10.1021/ja012474j
  45. Hanai, T.; Koizumi, K.; Kinoshita, T. J. Liq. Chromatogr. Relat. Technol. 2000, 23, 363 https://doi.org/10.1081/JLC-100101457
  46. Ma, Y.; Gross, K. C.; Hollingsworth, C. A.; Seybold, P. G.; Murray, J. S. J. Mol. Model 2004, 10, 235
  47. HyperChem, Release 7.0 for Windows, Molecular Modeling System; Hypercube Inc.: 2002
  48. Todeschini, R.; Consonni, V.; Pavan, M. Dragon Software, Version 2.1; 2002
  49. Dean, J. A. Lange's Handbook of Chemistry, 15th Ed.; McGraw- Hill, Inc.: 1999
  50. Demuth, H.; Beale, M. Neural Network Toolbox; Mathworks: Natick, MA, 2000
  51. Despagne, F.; Massart, D. L. Analyst 1998, 123, 157R https://doi.org/10.1039/a805562i
  52. Matlab 6.5; Mathworks: 1984-2002
  53. Famini, G. R.; Wilson, L. Y. J. Phys. Org. Chem. 1999, 12, 645 https://doi.org/10.1002/(SICI)1099-1395(199908)12:8<645::AID-POC165>3.0.CO;2-S

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