Application of GA-MLR method in QSPR modeling of stability constants of diverse 15-crown-5 complexes with sodium cation

  • Shahin Ahmadi
Original Article


A genetic algorithm based multiple linear regressions (GA-MLR) method was applied for quantitative structure property relationship (QSPR) modeling of stability constants for 65 complexes of 1,4,7,10,13-pentaoxacyclopentadecane ethers (15C5) with sodium cation (Na+). The best subset of molecular descriptors was selected with genetic algorithm subset selection procedure, to a variety of theoretical molecular descriptors, calculated by the Dragon software. The MLR model was developed with particular attention to external validation and applicability domain (AD). The validation was performed on the internal and external validation sets. The QSPR model presented in this study showed most accurate predictions with the leave one out cross validated variance (\( {\text{Q}}_{\text{loo - cv}}^{2} \) = 0.88) and the external-validated variance (\( Q_{\rm ext}^{2} \) = 0.82). The AD of the models was analysed by the leverage approach.


15-Crown-5 ethers Stability constant QSPR Genetic algorithm MLR Applicability domain 



This work is supported by Islamic Azad University, Kermanshah Branch, Kermanshah, Iran.


  1. 1.
    Saleh, M.I., Kusrini, E., Fun, H.K., Yamin, B.M.: Structural and selectivity of 18-crown-6 ligand in lanthanide–picrate complexes. J. Organomet. Chem. 693, 2561–2571 (2008)CrossRefGoogle Scholar
  2. 2.
    Moriuchi-Kawakami, T., Aoki, R., Morita, K., Tsujioka, H., Fujimori, K., Shibutani, Y., Shono, T.: Conformational analysis of 12-crown-3 and sodium ion selectivity of electrodes based on bis(12-crown-3) derivatives with malonate spacers. Anal. Chim. Acta 480, 291–298 (2003)CrossRefGoogle Scholar
  3. 3.
    Takeda, Y., Yasui, A., Katsuta, S.: Extraction of sodium and potassium perchlorates with dibenzo-18-crown-6 into various organic solvents. Quantitative elucidation of anion effects on the extraction-ability and -selectivity. J. Incl. Phenom. Macrocycl. Chem. 50, 157–164 (2004)CrossRefGoogle Scholar
  4. 4.
    Kim, H.S., Chi, K.W.: Monte Carlo simulation study for QSPR of solvent effect on the selectivity of 18-crown-6 between Gd3+ and Yb3+ ions. J. Mol. Struct. Theochem. 722, 1–7 (2005)CrossRefGoogle Scholar
  5. 5.
    Kim, H.S.: QSPR analysis of solvent effect on selectivity of 18-crown-6 between Nd3+ and Eu3+ ions: a Monte Carlo simulation study. Bull. Korean Chem. Soc. 27, 2011–2018 (2006)CrossRefGoogle Scholar
  6. 6.
    Kim, H.S.: QSPR analysis of solvent effect on selectivity of 18-crown-6 between Nd3+ and Eu3+ ions: a Monte Carlo simulation study. Abstr. Pap. Am. Chem. Soc. 230, U1327–U1328 (2005)Google Scholar
  7. 7.
    Yazdi, A.S., Mofazzeli, F., Es’haghi, Z.: Determination of 3-nitroaniline in water samples by directly suspended droplet three-phase liquid-phase microextraction using 18-crown-6 ether and high-performance liquid chromatography. J. Chromatogr. A 1216, 5086–5091 (2009)CrossRefGoogle Scholar
  8. 8.
    Parat, C., Betelu, S., Authier, L., Potin-Gautier, M.: Determination of labile trace metals with screen-printed electrode modified by a crown-ether based membrane. Anal. Chim. Acta 573, 14–19 (2006)CrossRefGoogle Scholar
  9. 9.
    Raut, D.R., Mohapatra, P.K., Ansari, S.A., Sarkar, A., Manchanda, V.K.: Selective transport of radio-cesium by supported liquid membranes containing calix[4]crown-6 ligands as the mobile carrier. Desalination 232, 262–271 (2008)CrossRefGoogle Scholar
  10. 10.
    Heng, L.Y., Hall, E.A.H.: Assessing a photocured self-plasticised acrylic membrane recipe for Na+ and K+ ion selective electrodes. Anal. Chim. Acta 443, 25–40 (2001)CrossRefGoogle Scholar
  11. 11.
    Han, W.S., Lee, Y.H., Jung, K.J., Ly, S.Y., Hong, T.K., Kim, M.H.: Potassium ion-selective polyaniline solid-contact electrodes based on 4′,4″(5″)-di-tert-butyldibenzo-18-crown-6-ether ionophore. J. Anal. Chem. 63, 987–993 (2008)CrossRefGoogle Scholar
  12. 12.
    Zeng, X.S., Han, X.X., Chen, L.X., Li, Q.S., Xu, F.B., He, X.W., Zhang, Z.Z.: The first synthesis of a calix[4](diseleno)crown ether as a sensor for ion-selective electrodes. Tetrahedron Lett. 43, 131–134 (2002)CrossRefGoogle Scholar
  13. 13.
    Pozzi, G., Quici, S., Fish, R.H.: Perfluorocarbon soluble crown ethers as phase transfer catalysts. Adv. Synth. Catal. 350, 2425–2436 (2008)CrossRefGoogle Scholar
  14. 14.
    Xia, L.X., Jia, Y., Tong, S.R., Wang, J., Han, G.X.: Interfacial behavior of phase transfer catalysis of the reaction between potassium thiocyanate and p-nitrobenzyl bromide with crown ethers as catalysts. Kinet. Catal. 51, 69–74 (2010)CrossRefGoogle Scholar
  15. 15.
    Jaszay, Z., Pham, T.S., Nemeth, G., Bako, P., Petnehazy, I., Toke, L.: Asymmetric synthesis of substituted alpha-amino phosphonates with chiral crown ethers as catalysts. Synlett. 9, 1429–1432 (2009)Google Scholar
  16. 16.
    Seki, A., Motoya, K., Watanabe, S., Kubo, I.: Novel sensors for potassium, calcium and magnesium ions based on a silicon transducer as a light-addressable potentiometric sensor. Anal. Chim. Acta 382, 131–136 (1999)CrossRefGoogle Scholar
  17. 17.
    Katritzky, A.R., Chen, K., Maran, U., Carlson, D.A.: QSPR correlation and predictions of GC retention indexes for methyl-branched hydrocarbons produced by insects. Anal. Chem. 72, 101–109 (2000)CrossRefGoogle Scholar
  18. 18.
    Ghasemi, J.B., Ahmadi, S., Brown, S.D.: A quantitative structure-retention relationship study for prediction of chromatographic relative retention time of chlorinated monoterpenes. Environ. Chem. Lett. (2009) (in press)Google Scholar
  19. 19.
    Fang, L., Huang, J., Yu, G., Li, X.: Quantitative structure-property relationship studies for direct photolysis rate constants and quantum yields of polybrominated diphenyl ethers in hexane and methanol. Ecotoxicol. Environ. Saf. 72, 1587–1593 (2009)CrossRefGoogle Scholar
  20. 20.
    Ghasemi, J., Ahmadi, S.: Combination of genetic algorithm and partial least squares for cloud point prediction of nonionic surfactants from molecular structures. Ann. Chim. Rome 97, 69–83 (2007)CrossRefGoogle Scholar
  21. 21.
    Tetko, I.V., Solov’ev, V.P., Antonov, A.V., Yao, X., Doucet, J.P., Fan, B., Hoonakker, F., Fourches, D., Jost, P., Lachiche, N., Varnek, A.: Benchmarking of linear and nonlinear approaches for quantitative structure–property relationship studies of metal complexation with ionophores. J. Chem. Inf. Model. 46, 808–819 (2006)CrossRefGoogle Scholar
  22. 22.
    Yao, X.J., Fan, B.T., Doucet, J.P., Panaye, A., Liu, M.C., Zhang, R.S., Zhang, X.Y., Hu, Z.D.: Quantitative structure property relationship models for the prediction of liquid heat capacity. QSAR Comb. Sci. 22, 29–48 (2003)CrossRefGoogle Scholar
  23. 23.
    Gakh, A.A., Sumpter, B.G., Noid, D.W., Sachleben, R.A., Moyer, B.A.: Prediction of complexation properties of crown ethers using computational neural networks. J. Incl. Phenom. Mol. Recognit. Chem. 27, 201–213 (1997)CrossRefGoogle Scholar
  24. 24.
    Shi, Z.G., Mccullough, E.A.: A computer simulation statistical procedure for predicting complexation equilibrium constants. J. Incl. Phenom. Mol. Recognit. Chem. 18, 9–26 (1994)CrossRefGoogle Scholar
  25. 25.
    Varnek, A., Wipff, G., Solov’ev, V.P., Solotnov, A.F.: Assessment of the macrocyclic effect for the complexation of crown-ethers with alkali cations using the substructural molecular fragments method. J. Chem. Inf. Comput. Sci. 42, 812–829 (2002)Google Scholar
  26. 26.
    Ghasemi, J., Saaidpour, S.: QSPR modeling of stability constants of diverse 15-crown-5 ethers complexes using best multiple linear regression. J. Incl. Phenom. Macrocycl. Chem. 60, 339–351 (2008)CrossRefGoogle Scholar
  27. 27.
    Leardi, R., Boggia, R., Terrile, M.: Genetic algorithms as a strategy for feature-selection. J. Chemom. 6, 267–281 (1992)CrossRefGoogle Scholar
  28. 28.
    Todeschini, R., Consonni, V., Mauri, A., Pavan, M.: Detecting “bad” regression models: multicriteria fitness functions in regression analysis. Anal. Chim. Acta 515, 199–208 (2004)CrossRefGoogle Scholar
  29. 29.
    Izatt, R.M., Pawlak, K., Bradshaw, J.S., Bruening, R.L.: Thermodynamic and kinetic data for macrocycle interaction with cations and anions. Chem. Rev. 91, 1721–2085 (1991)CrossRefGoogle Scholar
  30. 30.
    Hyperchem, v.7.5. Hypercube Inc. (2002)
  31. 31.
    Dewar, M.J.S., Zoebisch, E.G., Healy, E.F., Stewart, J.J.P.: AM1—a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc. 107, 3902–3909 (1985)CrossRefGoogle Scholar
  32. 32.
    Stewart, J.J.P.: Mopac 6.0, Quantum chemical program exchange. (1990)Google Scholar
  33. 33.
    Talete, S.: Dragon for windows (software for molecular descriptor calculations), version 5.4. (2006)
  34. 34.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989)Google Scholar
  35. 35.
    Goodarzi, M., Freitas, M.P., Wu, C.H., Duchowicz, P.R.: pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression. Chemom. Intell. Lab. 101, 102–109 (2010)CrossRefGoogle Scholar
  36. 36.
    Cho, S.J., Hermsmeier, M.A.: Genetic algorithm guided selection: variable selection and subset selection. J. Chem. Inf. Comput. Sci. 42, 927–936 (2002)Google Scholar
  37. 37.
    Gharagheizi, F., Alamdari, R.F.: Prediction of flash point temperature of pure components using a Quantitative Structure–Property Relationship model. QSAR Comb. Sci. 27, 679–683 (2008)CrossRefGoogle Scholar
  38. 38.
    Rogers, D., Hopfinger, A.J.: Application of genetic function approximation to quantitative structure–activity relationships and quantitative structure–property relationships. J. Chem. Inf. Comput. Sci. 34, 854–866 (1994)Google Scholar
  39. 39.
    Hemmateenejad, B., Miri, R., Akhond, M., Shamsipur, M.: QSAR study of the calcium channel antagonist activity of some recently synthesized dihydropyridine derivatives. An application of genetic algorithm for variable selection in MLR and PLS methods. Chemom. Intell. Lab. 64, 91–99 (2002)CrossRefGoogle Scholar
  40. 40.
    Depczynski, U., Frost, V.J., Molt, K.: Genetic algorithms applied to the selection of factors in principal component regression. Anal. Chim. Acta 420, 217–227 (2000)CrossRefGoogle Scholar
  41. 41.
    Jouanrimbaud, D., Massart, D.L., Leardi, R., Denoord, O.E.: Genetic algorithms as a tool for wavelength selection in multivariate calibration. Anal. Chem. 67, 4295–4301 (1995)CrossRefGoogle Scholar
  42. 42.
    Atkinson, A.C.: Plots, Transformations and Regression. Clarendon Press, Oxford (1985)Google Scholar
  43. 43.
    Gramatica, P.: Principles of QSAR models validation: internal and external. QSAR Comb. Sci. 26, 694–701 (2007)CrossRefGoogle Scholar
  44. 44.
    Guha, R., Serra, J.R., Jurs, P.C.: Generation of QSAR sets with a self-organizing map. J. Mol. Graph. Model. 23, 1–14 (2004)CrossRefGoogle Scholar
  45. 45.
    Jaiswal, M., Khadikar, P.V., Scozzafava, A., Supuran, C.T.: Carbonic anhydrase inhibitors: the first QSAR study on inhibition of tumor-associated isoenzyme IX with aromatic and heterocyclic sulfonamides. Bioorg. Med. Chem. Lett. 14, 3283–3290 (2004)CrossRefGoogle Scholar
  46. 46.
    Shapiro, S., Guggenheim, B.: Inhibition of oral bacteria by phenolic compounds—Part 1. QSAR analysis using molecular connectivity. Quant. Struct. Act. Relatsh. 17, 327–337 (1998)CrossRefGoogle Scholar
  47. 47.
    Geary, R.C.: The contiguity ratio and statistical mapping. Incorp. Statist. 5, 115–145 (1954)Google Scholar
  48. 48.
    Consonni, V., Todeschini, R., Pavan, M.: Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J. Chem. Inf. Comput. Sci. 42, 682–692 (2002)Google Scholar
  49. 49.
    Consonni, V., Todeschini, R., Pavan, M., Gramatica, P.: Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies. J. Chem. Inf. Comput. Sci. 42, 693–705 (2002)Google Scholar
  50. 50.
    Deswal, S., Roy, N.: Quantitative structure activity relationship studies of aryl heterocycle-based thrombin inhibitors. Eur. J. Med. Chem. 41, 1339–1346 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Chemistry, Kermanshah BranchIslamic Azad UniversityKermanshahIran

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