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Virtual Screening and Molecular Design Based on Hierarchical Qsar Technology

  • Victor E. Kuz’minEmail author
  • A.G. Artemenko
  • Eugene N. Muratov
  • P.G. Polischuk
  • L.N. Ognichenko
  • A.V. Liahovsky
  • A.I. Hromov
  • E.V. Varlamova
Chapter
Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH, volume 8)

Abstract

This chapter is devoted to the hierarchical QSAR technology (HiT QSAR) based on simplex representation of molecular structure (SiRMS) and its application to different QSAR/QSPR tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) of the QSAR paradigm by a series of enhanced models based on molecular structure description (in a specific order from 1D to 4D). Actually, it’s a system of permanently improved solutions. Different approaches for domain applicability estimation are implemented in HiT QSAR. In the SiRMS approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality, and symmetry). The level of simplex descriptors detailed increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach presented are an ability to solve QSAR/QSPR tasks for mixtures of compounds, the absence of the “molecular alignment” problem, consideration of different physical–chemical properties of atoms (e.g., charge, lipophilicity), and the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of HiT QSAR was demonstrated by its comparison with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D–4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as the predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic, biological, etc. experiments. The HiT QSAR is realized as the suite of computer programs termed the “HiT QSAR” software that so includes powerful statistical capabilities and a number of useful utilities.

Keywords

HiT QSAR Simplex representation SiRMS 

Abbreviations

A/I/EVS

Automatic/Interactive/Evolutionary Variables Selection

ACE

Angiotensin Converting Enzyme

AchE

Acetylcholinesterase

CoMFA

Comparative Molecular Fields Analysis QSAR approach

CoMSIA

Comparative Molecular Similarity Indexes Analysis QSAR approach

DA

Applicability Domain

DSTP

dispirotripiperazine

EVA

Eigenvalue Analysis QSAR approach

GA

Genetic Algorithm

HiT QSAR

Hierarchical QSAR Technology

HQSAR

Hologram QSAR approach

HRV

Human Rhinovirus

HSV

Herpes Simplex Virus

MLR

Multiple Linear Regression statistical method

PLS

Partial Least Squares or Projection on Latent Structures statistical method

Q2

cross-validation determination coefficient

QSAR/QSPR

Quantitative Structure-Activity/Property Relationship

R2

determination coefficient for training set

R2test

determination coefficient for test set

SD

Simplex Descriptor

SI

Selectivity Index

SiRMS

Simplex Representation of Molecular Structure QSAR approach

TV

Trend-Vector statistical method

References

  1. 1.
    Ooms F (2000) Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry. Curr Med Chem 7:141–158Google Scholar
  2. 2.
    Thomas G (2008) Medicinal chemistry: An introduction, 2nd edn John Wiley & Sons Inc, New YorkGoogle Scholar
  3. 3.
    Artemenko AG, Muratov EN, Kuz’min VE et al. (2007) Identification of individual structural fragments of N,N'-(bis-5-nitropyrimidyl)dispirotripiperazine derivatives for cytotoxicity and antiherpetic activity allows the prediction of new highly active compounds. J Antimicrob Chemother 60:68–77CrossRefGoogle Scholar
  4. 4.
    Bailey TR, Diana GD, Kowalczyk PJ et al. (1992) Antirhinoviral activity of heterocyclic analogs of win 54954. J Med Chem 35:4628–4633CrossRefGoogle Scholar
  5. 5.
    Butina D, Gola JMR (2004) Modeling aqueous solubility. J Chem Inf Comp Sci 43:837–841Google Scholar
  6. 6.
    de Jonge MR, Koymans LM, Vinkers HM et al. (2005) Structure based activity prediction of HIV-1 reverse transcriptase inhibitors. J Med Chem 48:2176–2183CrossRefGoogle Scholar
  7. 7.
    Jenssen H, Gutteberg TJ, Lejon T (2005) Modelling of anti-HSV activity of lactoferricin analogues using amino acid descriptors. J Pept Sci 11:97–103CrossRefGoogle Scholar
  8. 8.
    Kovatcheva A, Golbraikh A, Oloff S et al. (2004) Combinatorial QSAR of ambergris fragrance compounds. J Chem Inf Comp Sci 44:582–595Google Scholar
  9. 9.
    Kubinyi H (1990) Quantitative structure–activity relationships (QSAR) and molecular modeling in cancer research. J Cancer Res Clin Oncol 116:529–537CrossRefGoogle Scholar
  10. 10.
    Kuz’min VE, Artemenko AG, Lozitska RN et al. (2005) Investigation of anticancer activity of macrocyclic Schiff bases by means of 4D-QSAR based on simplex representation of molecular structure. SAR QSAR Environ Res 16:219–230CrossRefGoogle Scholar
  11. 11.
    Kuz’min VE, Artemenko AG, Muratov EN et al. (2007) Quantitative structure–activity relationship studies of [(biphenyloxy)propyl]isoxazole derivatives – human rhinovirus 2 replication inhibitors. J Med Chem 50:4205–4213CrossRefGoogle Scholar
  12. 12.
    Muratov EN, Artemenko AG, Kuz’min VE et al. (2005) Investigation of anti-influenza activity using hierarchic QSAR technology on the base of simplex representation of molecular structure. Antivir Res 65:A62–A63Google Scholar
  13. 13.
    Verma RP, Hansch C (2006) Chemical toxicity on HeLa cells. Curr Med Chem 13:423–448CrossRefGoogle Scholar
  14. 14.
    Zhang S, Golbraikh A, Tropsha A (2006) The development of quantitative structure–binding affinity relationship (QSBR) models based on novel geometrical chemical descriptors of the protein–ligand interfaces. J Med Chem 49:2713–2724CrossRefGoogle Scholar
  15. 15.
    Selassie CD (2003) History of QSAR. In: Abraham DJ (ed) Burger’s medicinal chemistry and drug discovery. Wiley, New York, p 960Google Scholar
  16. 16.
    Cramer RD, Patterson DI, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape binding to carrier proteins. J Am Chem Soc 110:5959–5967CrossRefGoogle Scholar
  17. 17.
    Doweyko AM (1988) The hypothetical active site lattice. An approach to modeling active sites from data on inhibitor molecules. J Math Chem 31:1396–1406Google Scholar
  18. 18.
    Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indeces in comparative anaysis (CoMSIA) of molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146CrossRefGoogle Scholar
  19. 19.
    Kuz’min VE, Artemenko AG, Kovdienko NA et al. (2000) Lattice model for QSAR studies. J Mol Model 6:517–526CrossRefGoogle Scholar
  20. 20.
    Seel M, Turner DB, Wilett P (1999) HQSAR – a highly predictive QSAR technique based on molecular holograms. QSAR 18:245–252Google Scholar
  21. 21.
    Pavan M, Consonni V, Gramatica P et al. (2006) New QSAR modelling approach based on ranking models by genetic algorithms – variable subset selection (GA-VSS). In: Brüggeman R, Carlsen L (eds) Partial order in environmental sciences and chemistry. Springer Berlin Heidelberg, Berlin, pp 181–217CrossRefGoogle Scholar
  22. 22.
    Kuz’min VE, Muratov EN, Artemenko AG et al. (2008) The effect of nitroaromatics composition on theirs toxicity in vivo. 1D QSAR research. Chemosphere 72:1373–1380CrossRefGoogle Scholar
  23. 23.
    Baurin N, Mozziconacci JC, Arnoult E et al. (2004) 2D QSAR consensus prediction for high-throughput virtual screening. An application to COX-2 inhibition modeling and screening of the NCI database. J Chem Inf Model 44:276–285CrossRefGoogle Scholar
  24. 24.
    Vedani A, Dobler M (2000) Multi-dimensional QSAR in drug design. Progress in Drug Res 55:107–135Google Scholar
  25. 25.
    Artemenko A, Kuz’min V, Muratov E et al. (2007) Molecular design of active antiherpetic compounds using hierarchic QSAR technology. Antivir Res 74:A76CrossRefGoogle Scholar
  26. 26.
    Artemenko A, Muratov E, Kuz’min V et al. (2006) Molecular design of novel antimicrobial agents on the base of 4-thiazolidone derivatives. Clin Microbiol Infec 12:1557Google Scholar
  27. 27.
    Artemenko A, Muratov E, Kuz’min V et al. (2006) Influence of artifical ribonucleases structure on their anti-HIV activity. Antivir Res 70:A43Google Scholar
  28. 28.
    Artemenko AG, Kuz’min VE, Muratov EN et al. (2005) Investigation of antiherpetic activity using hierarchic QSAR technology on the base of simplex representation of molecular structure. Antivir Res 65:A77Google Scholar
  29. 29.
    Kuz’min VE, Artemenko AG, Lozitsky VP et al. (2002) The analysis of structure-anticancer and antiviral activity relationships for macrocyclic pyridinophanes and their analogues on the basis of 4D QSAR models (simplex representation of molecular structure). Acta Biochim Polon 49:157–168Google Scholar
  30. 30.
    Kuz’min VE, Artemenko AG, Muratov EN et al. (2007) QSAR analysis of anti-coxsackievirus B3 nancy activity of 2-amino-3-nitropyrazole[1,5-α]pyrimidines by means of simplex approach. Antivir Res 74:A49–A50CrossRefGoogle Scholar
  31. 31.
    Kuz’min VE, Artemenko AG, Muratov EN et al. (2005) The hierarchical QSAR technology for effective virtual screening and molecular design of the promising antiviral compounds. Antivir Res 65:A70–A71Google Scholar
  32. 32.
    Kuz’min VE, Artemenko AG, Polischuk PG et al. (2005) Hierarchic system of QSAR models (1D-4D) on the base of simplex representation of molecular structure. J Mol Model 11:457–467CrossRefGoogle Scholar
  33. 33.
    Muratov E, Artemenko A, Kuz’min V et al. (2006) Computational design of the new antimicrobials based on the substituted crown ethers. Clin Microbiol Infec 12:1558Google Scholar
  34. 34.
    Muratov EN (2004) Quantitative evaluation of the structural factors influence on the properties of nitrogen-, oxygen- and sulfur-containing macroheterocycles. National Academy of Sciences of Ukraine, A.V. Bogatsky Physical-Chemical Institute, Odessa, p 202Google Scholar
  35. 35.
    Muratov EN, Kuz’min VE, Artemenko AG et al. (2006) QSAR studies demonstrate the influence of structure of [(biphenyloxy)propyl]isoxazole derivatives on inhibition of coxsackievirus B3 (CVB3) replication. Antivir Res 70:A77Google Scholar
  36. 36.
    Kuz’min VE, Artemenko AG, Muratov EN (2008) Hierarchical QSAR technology on the base of simplex representation of molecular structure. J Comp Aid Mol Des 22:403–421CrossRefGoogle Scholar
  37. 37.
    Kuz’min VE, Muratov EN, Artemenko AG et al. (2008) The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study. J Comp Aid Mol Des 22:747–759. doi:10.1007/s10822-10008-19211-xCrossRefGoogle Scholar
  38. 38.
    QSAR, Expert, Group (2004) The report from the expert group on (quantitative) structure–activity relationships [(Q)SARs] on the principles for the validation of (Q)SARs. In: OECD series on testing and assessment. Organisation for Economic Co-operation and Development, Paris, p 206Google Scholar
  39. 39.
    Kuz’min VE (1995) About homo- and heterochirality of dissymetrical tetrahedrons (chiral simplexes). Stereochemical tunneling. Zh Strucur Khim (in Russ) 36:873–878Google Scholar
  40. 40.
    Jolly WL, Perry WB (1973) Estimation of atomic charges by an electronegativity equalization procedure calibration with core binding energies. J Am Chem Soc 95:5442–5450CrossRefGoogle Scholar
  41. 41.
    Wang R, Fu Y, Lai L (1997) A new atom-additive method for calculating partition coefficients. J Chem Inf Comp Sci 37:615–621Google Scholar
  42. 42.
    Ioffe BV (1983) Chemistry refractometric methods, 3 ed. Himiya, LeningradGoogle Scholar
  43. 43.
    Cahn RS, Ingold CK, Prelog V (1966) Specification of molecular chirality. Angew Chem Int Ed 5:385–415CrossRefGoogle Scholar
  44. 44.
    Burkert U, Allinger N (1982) Molecular mechanics. ACS Publication, Washington, DCGoogle Scholar
  45. 45.
    Hodges G, Roberts DW, Marshall SJ et al. (2006) Defining the toxic mode of action of ester sulphonates using the joint toxicity of mixtures. Chemosphere 64:17–25CrossRefGoogle Scholar
  46. 46.
    Kuz’min VE, Muratov EN, Artemenko AG et al. (2009) Consensus QSAR modeling of phosphor-containing chiral AChE inhibitors. J Comp Aid Mol Des 28:664–677Google Scholar
  47. 47.
    Hyperchem 7.5 software. Hypercube, Inc. 1115 NW 4th Street, Gainesville, FL 32601, USAGoogle Scholar
  48. 48.
    Kuz’min VE, Artemenko AG, Kovdienko NA et al. (1999) Lattice models of molecules for solution of QSAR tasks. Khim-Pharm Zhurn (in Russ) 9:14–20Google Scholar
  49. 49.
    Kuz’min VE, Beresteckaja EL (1983) The program for calculation of atom charges using the method of orbital electronegativities equalization. Zh Struct Khimii (in Russ) 24:187–188Google Scholar
  50. 50.
    Croizet F, Langlois MH, Dubost JP et al. (1990) Lipophilicity force field profile: An expressive visualization of the lipophilicity molecular potential gradient. J Mol Graphics 8:53Google Scholar
  51. 51.
    Artemenko AG, Kovdienko NA, Kuzmin VE et al. (2002) The analysis of “structure-anticancer activity” relationship in a set of macrocyclic pyridinophanes and their acyclic analogues on the basis of lattice model of molecule using fractal parameters. Exp Oncol 24:123–127Google Scholar
  52. 52.
    Lozitsky VP, Kuz’min VE, Artemenko AG et al. (2000) The analysis of structure–anti-influenza relationship on the basis molecular lattice model for macrocyclic piridino-phanes and their analogs. Antivir Res 50:A85Google Scholar
  53. 53.
    Marple SL Jr (1987) Digital spectral analysis with applications. Prentice-Hall Inc., Englewood Cliffs, NJGoogle Scholar
  54. 54.
    Kuz’min VE, Trigub LP, Shapiro YE et al. (1995) The parameters of shape of peptide molecules as a descriptors in the QSAR tasks. Zh Struct Khimii (in Russ) 36:509–517Google Scholar
  55. 55.
    Breiman L, Friedman JH, Olshen RA et al. (1984) Classification and regression trees. Wadsworth, BelmontGoogle Scholar
  56. 56.
    Carhart RE, Smith DH, Venkataraghavan R (1985) Atom pairs as molecular features in structure–activity studies. Definition and application. J Chem Inf Comput Sci 25:64–73Google Scholar
  57. 57.
    Vitiuk NV, Kuz’min VE (1994) Mechanistic models in chemometrics for the analysis of multidimensional data of researches. Analogue of dipole-moments method in the structure(composition)–property relationships analysis. ZhAnalKhimii 49:165–167Google Scholar
  58. 58.
    Ferster E, Renz B (1979) Methoden der Korrelations und Regressionanalyse. Verlag Die Wirtschaft, BerlinGoogle Scholar
  59. 59.
    Topliss JG, Costello RJ (1972) Chance correlations in structure–activity studies using multiple regression analysis. J Med Chem 15:1066–1068CrossRefGoogle Scholar
  60. 60.
    Kubinyi H (1996) Evolutionary variable selection in regression and PLS analyses. J Chemometr 10:119–133CrossRefGoogle Scholar
  61. 61.
    Lindgren F, Geladi P, Rannar S et al. (1994) Interactive variable selection (IVS) for PLS. Part 1: Theory and algorithms. J Chemometr 8:349–363CrossRefGoogle Scholar
  62. 62.
    Rannar S, Lindgren F, Geladi P et al. (1994) A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm. J Chemometr 8:111–125CrossRefGoogle Scholar
  63. 63.
    Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative structure–activity relationships and quantitative structure–property relationships. J Chem Inf Comp Sci 34:854–866Google Scholar
  64. 64.
    Wold S, Antti H, Lindgren F et al. (1998) Orthogonal signal correction of nearinfrared spectra. Chemometrics Intell Lab Syst 44:175–185CrossRefGoogle Scholar
  65. 65.
    Trygg J, Wold S (2002) Orthogonal projections to latent structures (O-PLS). J Chemometr 16:119–128CrossRefGoogle Scholar
  66. 66.
    Cronin MTD, Schultz TW (2003) Pitfalls in QSAR. J Mol Struct (Theochem) 622:39–51CrossRefGoogle Scholar
  67. 67.
    Zhang S, Golbraikh A, Oloff S et al. (2006) A novel automated lazy learning QSAR (ALL-QSAR) approach: Method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J Chem Inf Model 46:1984–1995CrossRefGoogle Scholar
  68. 68.
    Neter J, Kutner MH, Wasseman W et al. (1996) Applied linear statistical models. McGraw-Hill, New YorkGoogle Scholar
  69. 69.
    Meloun M, Militku J, Hill M et al. (2002) Crucial problems in regression modelling and their solutions. Analyst 127:433–450CrossRefGoogle Scholar
  70. 70.
    Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicability domain estimation by projection of the training set in descriptor space: A review. Altern Lab Anim 33:445–459Google Scholar
  71. 71.
    Östergard PRJ (2002) A fast algorithm for the maximum clique problem. Discrete Appl Math 120:195–205CrossRefGoogle Scholar
  72. 72.
    Bodor N, Buchwald P (2000) Soft drug design: General principles and recent applications. Med Res Rev 20:58–101CrossRefGoogle Scholar
  73. 73.
    Sutherland JJ, O’Brien LA, Weaver DF (2004) A comparison of methods for modeling quantitative structure–activity relationships. J Med Chem 47:5541–5554CrossRefGoogle Scholar
  74. 74.
    Heritage TV, Ferguson AM, Turner DB et al. (1998) EVA: A novel theoretical descriptor for QSAR studies. Persp Drug Disc Des 11:381–398CrossRefGoogle Scholar
  75. 75.
    Barnard DL (2006) Current status of anti-picornavirus therapies. Curr Pharm Des 12:1379–1390CrossRefGoogle Scholar
  76. 76.
    Patick AK (2006) Rhinovirus chemotherapy. Antivir Res 71:391–396CrossRefGoogle Scholar
  77. 77.
    Rotbart HA (2002) Treatment of picornavirus infections. Antivir Res 53:83–98CrossRefGoogle Scholar
  78. 78.
    Binford SL, Maldonado F, Brothers MA et al. (2005) Conservation of amino acids in human rhinovirus 3C protease correlates with broad-spectrum antiviral activity of rupintrivir, a novel human rhinovirus 3C protease inhibitor. Antimicrob Agents Chemother 49:619–626CrossRefGoogle Scholar
  79. 79.
    Conti C, Mastromarino P, Goldoni P et al. (2005) Synthesis and anti-rhinovirus properties of fluoro-substituted flavonoids. Antivir Chem Chemother 16:267–276Google Scholar
  80. 80.
    Cutri CC, Garozzo A, Siracusa MA et al. (2002) Synthesis of new 3-methylthio-5-aryl-4-isothiazolecarbonitriles with broad antiviral spectrum. Antiviral Res 55:357–368CrossRefGoogle Scholar
  81. 81.
    Diana GD, Cutcliffe D, Oglesby RC et al. (1989) Synthesis and structure–activity studies of some disubstituted phenylisoxazoles against human picornavirus. J Med Chem 32:450–455CrossRefGoogle Scholar
  82. 82.
    Dragovich PS, Prins TJ, Zhou R et al. (2002) Structure-based design, synthesis, and biological evaluation of irreversible human rhinovirus 3C protease inhibitors. 6. Structure-activity studies of orally bioavailable, 2-pyridone-containing peptidomimetics. J Med Chem 45:1607–1623CrossRefGoogle Scholar
  83. 83.
    Gaudernak E, Seipelt J, Triendl A et al. (2002) Antiviral effects of pyrrolidine dithiocarbamate on human rhinoviruses. J Virol 76:6004–6015CrossRefGoogle Scholar
  84. 84.
    Kaiser L, Crump CE, Hayden FG (2000) In vitro activity of pleconaril and AG7088 against selected serotypes and clinical isolates of human rhinoviruses. Antiviral Res 47:215–220CrossRefGoogle Scholar
  85. 85.
    Suchachev DV, Pivina TS, Shliapochnikov VA et al. (1993) Investigation of quantitative “structure-shock-sensitivity” relationships for organic polynitrous compounds. Dokl RAN (in Russ) 328:50–57Google Scholar
  86. 86.
    Kuz’min VE, Lozitsky VP, Kamalov GL et al. (2000) The analysis of “structure–anticancer activity” relationship in a set of macrocyclic 2,6-bis (2- and 4-formylaryloxymethyl) pyridines Schiff bases. Acta Biochim Polon 47:867–875Google Scholar
  87. 87.
    Kuz’min VE, Muratov EN, Artemenko AG et al. (2008) The effect of nitroaromatics’ composition on their toxicity in vivo: Novel, efficient non-additive 1D QSAR analysis. Chemosphere 72(9):1373–1380. doi:10.1016/j.chemosphere.2008.1004.1045CrossRefGoogle Scholar
  88. 88.
    Katritzky AR, Oliferenko P, Oliferenko A et al. (2003) Nitrobenzene toxicity: QSAR correlations and mechanistic interpretations. J Phys Org Chem 16:811–817CrossRefGoogle Scholar
  89. 89.
    Chilmonczyk Z, Szelejewska-Wozniakowska A, Cybulski J et al. (1997) Conformational flexibility of serotonin1A receptor ligands from crystallographic data. Updated model of the receptor pharmacophore. Archiv der Pharmazie 330:146–160CrossRefGoogle Scholar
  90. 90.
    Hibert MF, Gittos MW, Middlemiss DN et al. (1988) Graphics computer-aided receptor mapping as a predictive tool for drug design: Development of potent, selective, and stereospecific ligands for the 5-HTlA receptor. J Med Chem 31:1087–1093CrossRefGoogle Scholar
  91. 91.
    Hibert MF, Mcdermott I, Middlemiss DN et al. (1989) Radioligand binding study of a series of 5-HT1A receptor agonists and definition of a steric model of this site. Eur J Med Chem 24:31–37CrossRefGoogle Scholar
  92. 92.
    Kuz’min VE, Polischuk PG, Artemenko AG et al. (2008) Quantitative structure–affinity relationship of 5 HT1A receptor ligands by the classification tree method. SAR & QSAR in Env Res 19:213–244CrossRefGoogle Scholar
  93. 93.
    Todeschini R, Consonni V (2000) Handbook of molecular descriptors, 1st ed. Wiley-VCH, WeinheimCrossRefGoogle Scholar
  94. 94.
    Artemenko AGKuz’min VE Muratov EN et al. (2009) The analysis of influence of benzodiazepine derivatives structure on its pharmacocinetic properties. Khim-Pharm Zhurn 43:36–45 (in Russ)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Victor E. Kuz’min
    • 1
    Email author
  • A.G. Artemenko
    • 1
  • Eugene N. Muratov
    • 1
  • P.G. Polischuk
    • 1
  • L.N. Ognichenko
    • 1
  • A.V. Liahovsky
    • 1
  • A.I. Hromov
    • 1
  • E.V. Varlamova
    • 1
  1. 1.A.V. Bogatsky Physical-Chemical Institute NAS of UkraineOdessaUkraine

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