Neural Networks in Analytical Chemistry

  • Mehdi Jalali-Heravi
Part of the Methods in Molecular Biology™ book series (MIMB, volume 458)

Abstract

This chapter covers a part of the spectrum of neural-network uses in analytical chemistry. Different architectures of neural networks are described briefly. The chapter focuses on the development of three-layer artificial neural network for modeling the anti-HIV activity of the HETP derivatives and activity parameters (pIC50) of heparanase inhibitors. The use of a genetic algorithm-kernel partial least squares algorithm combined with an artificial neural network (GA-KPLS-ANN) is described for predicting the activities of a series of aromatic sulfonamides. The retention behavior of terpenes and volatile organic compounds and predicting the response surface of different detection systems are presented as typical applications of ANNs in chromatographic area. The use of ANNs is explored in electrophoresis with emphasizes on its application on peptide mapping. Simulation of the electropherogram of glucagons and horse cytochrome C is described as peptide models. This chapter also focuses on discussing the role of ANNs in the simulation of mass and 13C-NMR spectra for noncyclic alkenes and alkanes and lignin and xanthones, respectively.

Keywords

Artificial neural network supervised network unsupervised network self-organizing maps QSAR chromatographic parameters electrophoretic mobility peptide mapping simulation of spectra sulfonamides heparanase inhibitors terpenes response factor lignins xanthones 

References

  1. 1.
    McCulloch WS, Pitts W (1943) A statistical consequence of the logical calculus of nervous nets. Bull Math Biolophys 5:115–113.CrossRefGoogle Scholar
  2. 2.
    McCulloch WS, Pitts W (1947) The limiting information capacity of a neuronal link. Bull Math Biolophys 9:127–147.CrossRefGoogle Scholar
  3. 3.
    Hebb DO (1949) The organization of behavior. Wiley, New York.Google Scholar
  4. 4.
    Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci 79:2554–2567.CrossRefPubMedGoogle Scholar
  5. 5.
    Haykin S (1994) Neural network. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
  6. 6.
    Zupan J, Gasteiger J (1999), Neural networks in chemistry and drug design. Wiley-VCH, Weinheim.Google Scholar
  7. 7.
    Bose NK, Liang P (1996), Neural networks, fundamentals. McGraw-Hill, New York.Google Scholar
  8. 8.
    Anker SL, Jurs PC (1992) Application of neural networks in structure-activity relationships. Anal Chem 64:1157–1165.CrossRefGoogle Scholar
  9. 9.
    Hagan MT, Demuth HB, Beal M (1996) Neural network design. PWS Publishing, Boston.Google Scholar
  10. 10.
    Zupan J, Gasteiger J (1993) Neural networks for chemists, an introduction. VCH, Weinheim.Google Scholar
  11. 11.
    Hopke PK, Song X (1997) Source apportionment of soil samples by the combination of two neural networks based on computer. Anal Chim Acta 348:375–386.CrossRefGoogle Scholar
  12. 12.
    Lippmann RP (1987) IEEE ASSP (April 4).Google Scholar
  13. 13.
    Tusar M, Zupan J (1990) Software development in chemistry 4. In: Gasteiger J (ed) Neural networks,. Springer, Berlin, pp. 363–376.Google Scholar
  14. 14.
    Kohonen T (1982) Analysis of a simple self-organizing process. Biol Cybernetics 43:59–69.CrossRefGoogle Scholar
  15. 15.
    Kohonen T (1988) Self-organization and associate memory. Springer, Berlin.Google Scholar
  16. 16.
    Zupan J (1989) Algorithms for chemists. Wiley, Chichester, UK, pp. 257–262.Google Scholar
  17. 17.
    Todeschini R, Consonni V, Pavan M (2002) Dragon software, version 2.1, via pisani, 13-20124 Milan, Italy.Google Scholar
  18. 18.
    Jalali-Heravi, M, Parastar F (2000) Use of artificial neural networks in a QSAR study of anti-HIV activity for a large group of HEPT derivatives. J Chem Inf Comput Sci 40:147–154.PubMedGoogle Scholar
  19. 19.
    Luco JM, Ferretti FH (1997) QSAR based on multiple linear regression and PLS methods for the anti-HIV activity of a large group of HEPT derivatives. J Chem Inf Comput Sci 37:392–401.PubMedGoogle Scholar
  20. 20.
    Tanaka H, Takashima H, Ubasawa M, Sekiya K, Nitta I, Baba M, Shigeta S, Walker RT, Clercq ED, Miyasaka T (1992) Synthesis and antiviral activity of deoxy analogs of 1-[(2-Hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) as potent and selective anti-HIV-1 agents. J Med Chem 35:4713–4719.CrossRefPubMedGoogle Scholar
  21. 21.
    Hansch C, Muir R M, Fujita T, Maloney PP, Geiger F, Streich M (1963) The correlation of biological activity of plant growth regulators and chloromycetin derivatives with Hammett constants and partition coefficients. J Am Chem Soc 85:2817– 2824.CrossRefGoogle Scholar
  22. 22.
    Hansch C, Hoekman D, Gao H (1996) Comparative QSAR: toward a deeper understanding of chemicobiological interactions. Chem Rev 96:1045–1075.CrossRefPubMedGoogle Scholar
  23. 23.
    Katritzky AR, Labanov VS, Karelson M (Copyright 1994–1995) CODESSA 2.0, Comprehensive descriptors for structural and statistical analysis. University of Florida, Gainesville.Google Scholar
  24. 24.
    Jalali-Heravi M, Kyani A (2007) Application of genetic algorithm-kernel partial least square as a novel nonlinear feature selection method: activity of carbonic anhydrase II inhibitors. Eur J Med Chem 42:649–659.CrossRefPubMedGoogle Scholar
  25. 25.
    Agrawal VK, Bano S, Supuran CT, Khadikar PV (2004) QSAR study on carbonic anhydrase inhibitors: aromatic/heterocyclic sulfonamides containing 8-quinoline-sulfonyl moieties, with topical activity as antiglaucoma agents. Eur J Med Chem 39:593–600.CrossRefPubMedGoogle Scholar
  26. 26.
    Melagraki G, Afantitis A, Sarimveis H, Igglessi-Markopoulou O, Supuran CT (2006) QSAR study on para-substituted aromatic sulfonamides as carbonic anhydrase II inhibitors using topological information indices. Bioorg Med Chem 14:1108–1114.CrossRefPubMedGoogle Scholar
  27. 27.
    Clare BW,. Supuran CT (1999) Carbonic anhydrase inhibitors. Part 61. Quantum chemical QSAR of a group of benzenedisulfonamides. Eur J Med Chem 34:463–474.CrossRefGoogle Scholar
  28. 28.
    Jalali-Heravi M, Garkani-Nejad Z (2001) Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks. J Chromatogr A 927:211–218.CrossRefPubMedGoogle Scholar
  29. 29.
    Vlodavsky I, Goldshmidt O, Zcharia E, Metzger S, Chajek-Shaulb T, Atzmon R, Guatta-Rangini Z, Friedmann,Y (2001) Molecular properties and involvement of heparanase in cancer progression and normal development. Biochimie 83:831-839.CrossRefPubMedGoogle Scholar
  30. 30.
    Bernfield M, Götte M, Woo Park P, Reizes O, Fitzgerald ML, Lincecum J, Zako, M (1999) Functions of cell surface heparin sulfate proteoglycans. Ann Rev Biochem 68:729–777.CrossRefPubMedGoogle Scholar
  31. 31.
    Vlodavsky I, Friedmann Y (2001) Molecular properties and involvement of heparanase in cancer metastasis and angiogenesis. J Clin Invest 108:341–347.PubMedGoogle Scholar
  32. 32.
    Courtney SM, Hay PA, Buck RT, Colville CS, Porter DW, Scopes DIC, Pollard FC, Page MJ, Bennett JM, Hircock ML, McKenzie EA, Stubberfield CR, Turner PR (2004) 2,3-Dihydro-1,3-dioxo-1H-isoindole-5-carboxylic acid derivatives: a novel class of small molecule heparanase inhibitors. Bioorg Med Chem Let 14:3269–3273.CrossRefGoogle Scholar
  33. 33.
    Courtney SM, Hay PA, Buck RT, Colville CS, Phillips DJ, Scopes DAC, Pollard FC, Page MJ, Bennett JM, Hircock ML, McKenzie EA, Bhaman M, Felix R, Stubberfield CR, Turner PR (2005) Furanyl-1,3-thiazol-2-yl and benzoxazol-5-yl acetic acid derivatives: novel classes of heparanase inhibitor. Bioorg Med Chem Let 15:2295–2299.CrossRefGoogle Scholar
  34. 34.
    Jalali-Heravi M, Asadollahi-Baboli M, Shahbazikhah P (2007) QSAR study of heparanase inhibitors activity using artificial neural networks and Levenberg-Marquardt algorithm. Eur J Med Chem (in press).Google Scholar
  35. 35.
    Jalali-Heravi M, Fatemi MH (1998) Prediction of flame ionization detector response factor using an artificial neural network. J Chromatogr A 825:161–169.CrossRefGoogle Scholar
  36. 36.
    Anker LS, Jurs PC, Edwards PA (1990) Quantitative structure-retention relationship studies of odor-active aliphatic compounds with oxygen-containing functional groups. Anal Chem 62:2676–2684.CrossRefPubMedGoogle Scholar
  37. 37.
    Jalali-Heravi M, Parastar F (2000) Development of comprehensive descriptors for multiple linear regression and artificial neural network modeling of retention behaviors of a variety of compounds on different stationary phases. J Chromatogr A 903:145–154.CrossRefPubMedGoogle Scholar
  38. 38.
    Kollie TO, Poole CF, Abraham MH, Whiting, G. S. (1992) Comparison of two free energy of solvation models for characterizing selectivity of stationary phases used in gas-liquid chromatography. Anal Chim Acta 259:1–13.CrossRefGoogle Scholar
  39. 39.
    Jalali-Heravi M, Fatemi MH (2001) Artificial neural network modeling of Kováts retention indices for noncyclic and monocyclic terpenes. J Chromatogr A 915:177–183.CrossRefPubMedGoogle Scholar
  40. 40.
    Jalali-Heravi M, Garakani-Nejad Z (2002) Use of self-training artificial neural networks in modeling of gas chromatographic relative retention times of a variety of organic compounds. J Cromatogr A 945:173–184.CrossRefGoogle Scholar
  41. 41.
    Wentworth WE, Helias, N, Zlatkis A, Chen ECM, Stearns SD (1998) Multiple detector responses for gas chromatography peak identification. J Chromatogr A 795:319–347.CrossRefGoogle Scholar
  42. 42.
    Jalali-Heravi M, Kyani A (2004) Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-MLR-ANN approach. J Chem Inf Comput Sci 44:1328–1335.PubMedGoogle Scholar
  43. 43.
    Jalali-Heravi M, Fatemi MH (2000) Prediction of thermal conductivity detection response factors using an artificial neural network. J Chromatogr A 897:227–235.CrossRefPubMedGoogle Scholar
  44. 44.
    Jalali-Heravi M, Garakani-Nejad Z (2002) Prediction of relative response factors for flame ionization and photoionization detection using self-training artificial neural networks. J Chromatogr A 950:183–194.CrossRefPubMedGoogle Scholar
  45. 45.
    Jalali-Heravi M, Noroozian E, Mousavi M (2004) Prediction of relative response factors of electron-capture detection for some polychlorinated biphenyls using chemometrics. J Chromatogr A 1023:247–254.CrossRefPubMedGoogle Scholar
  46. 46.
    Link AJ, Eng J, Schieltz DM, Carmack E, Mize GJ, Morris DR, Arvik BM, Yates JR (1999) Direct analysis of protein complexes using mass spectrometry. Nat Biotechnol 17:676–682.CrossRefPubMedGoogle Scholar
  47. 47.
    Wasburn MP, Wolters D, Yates JR (2001) Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol 19:242–247.CrossRefGoogle Scholar
  48. 48.
    Grossman PD, Colburn JC, Lauer HH (1989) A semiempirical model for the electrophoretic mobilities of peptides in free-solution capillary electrophoresis. Anal Biochem 179, 28–33.CrossRefPubMedGoogle Scholar
  49. 49.
    Offord RE (1996) Electrophoretic mobilities of peptides on paper and their use in the determination of amide groups. Nature 211:591–593.CrossRefGoogle Scholar
  50. 50.
    Compton BJ (1991) Electrophoretic mobility modeling of proteins in free zone capillary electrophoresis and its application to monoclonal antibody microheterogeneity analysis. J Chtomatogr A 599:357–366.CrossRefGoogle Scholar
  51. 51.
    Cifuentes A, Poppe H (1997) Behavior of peptides in capillary electrophoresis: effect of peptide charge, mass and structure. Electrophoresis 18:2362–2376.CrossRefPubMedGoogle Scholar
  52. 52.
    Janini GM, Mertal CJ, Issaq HJ, Muschik GM (1999) Peptide mobility and peptide mapping in capillary zone electrophoresis: experimental determination and theoretical simulation. J Chromatogr A 848:417–433.CrossRefPubMedGoogle Scholar
  53. 53.
    Jalali-Heravi M, Shen Y, Hassanisadi M, Khaledi MG (2005) Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the Offord model and artificial neural networks. Electrophoresis 26:1874–1885.CrossRefPubMedGoogle Scholar
  54. 54.
    Jalali-Heravi M, Shen Y, Hassanisadi M, Khaledi MG (2005) Artificial neural network modeling of peptide mobility and peptide mapping in capillary zone electrophoresis. J Chromatogr A 1096:58–68.CrossRefPubMedGoogle Scholar
  55. 55.
    Taft Jr., R.W., (1956) In: NewmanMS (ed) Organic chemistry. Wiley, New York.Google Scholar
  56. 56.
    Janini GM., Metral CJ, Issaq HJ (2001) Peptide mapping by capillary zone electrophoresis: how close is theoretical simulation to experimental determination. J Chromatogr A 924:291–306.CrossRefGoogle Scholar
  57. 57.
    Scopes RK (1974) Measurement of protein by spectrophotometry at 205 nm. Anal Biochem 59:277–282.CrossRefPubMedGoogle Scholar
  58. 58.
    Herold M, Ross GA, Grimm R, Heiger DN (1996) In: Altria KD (ed) Capillary electrophoresis guidebook: principles, operation, and applications, methods in molecular biology. Humana Press, Totowa, NJ.Google Scholar
  59. 59.
    Aires-de-Sousa J, Hemmer MC, Gasteiger, J. (2002) Prediction of 1H NMR chemical shifts using neural networks. Anal Chem 74:80–90.CrossRefPubMedGoogle Scholar
  60. 60.
    Ball JW, Anker LS, Jurs PC (1991) Automated model selection for the simulation of carbon-13 nuclear magnetic resonance spectra of cyclopentanones and cycloheptanones. Anal Chem 63:2435–2442.CrossRefGoogle Scholar
  61. 61.
    Jalali-Heravi M, Mousavi M (1995) Simulation of 13C NMR. spectra of nitrogen-containing aromatic compounds. Aust J Chem 48:12671275.Google Scholar
  62. 62.
    Meiler J, Will M (2001) Automated structure elucidation of organic molecules from 13C NMR spectra using genetic algorithms and neural networks. J Chem Inf Comp Sci 41:1535–2546.Google Scholar
  63. 63.
    Meiler J, Maier W, Will M, Meusinger R (2002) Using neural networks for 13C NMR chemical shift prediction—comparison with traditional methods. J Mag Reson 157:242–252.CrossRefGoogle Scholar
  64. 64.
    Meiler J, Will M (2002) Genius: a genetic algorithm for automated structure elucidation from 13C NMR spectra. J Am Chem Soc 124:1868–1870.CrossRefPubMedGoogle Scholar
  65. 65.
    Jalali-Heravi M, Masoum S, Shahbazikhah P (2004) Simulation of 13C nuclear magnetic resonance spectra of lignin compounds using principal component analysis and artificial neural networks. J Mag Reson 171:176–185.CrossRefGoogle Scholar
  66. 66.
  67. 67.
  68. 68.
    M, Jalali-Heravi P, Shahbazikhah BS, Zekavat M Ardejani (2007) Principal component analysis-ranking as a variable selection method for the simulation of 13C nuclear magnetic resonance spectra of xanthones using artificial neural networks. QSAR Comb Sci (in press).Google Scholar
  69. 69.
    Peres V, Nagem TJ. (1997) Trioxygenated naturally occurring xanthones. Phytochemistry 44:191–214.CrossRefGoogle Scholar
  70. 70.
    Peres V, Nagem TJ, Faustino de Oliveira F (2000) Tetraoxygenated naturally occurring xanthones. Phytochemistry 55:683–710.CrossRefPubMedGoogle Scholar
  71. 71.
    Jalali-Heravi M, Fatemi MH (2000) Simulation of mass spectra of noncyclic alkanes and alkenes using artificial neural network. Anal Chim Acta 415:95–103.CrossRefGoogle Scholar

Copyright information

© Humana Press, a part of Springer Science + Business Media, LLC 2008

Authors and Affiliations

  • Mehdi Jalali-Heravi
    • 1
  1. 1.Department of ChemistrySharif University of TechnologyIran

Personalised recommendations