Structural Chemistry

, Volume 24, Issue 6, pp 1873–1893 | Cite as

Variational principles for mechanistic quantitative structure–activity relationship (QSAR) studies: application on uracil derivatives’ anti-HIV action

  • Mihai V. Putz
  • Nicoleta A. Dudaş
Original Research


Mechanistic quantitative structure–activity relationships (QSAR) variational principles relating data screening and data analysis are mainly introduced as: the longest simplified molecular-input line-entry system—SMILES’ molecular chain (LoSMoC) to achieve for the maximum 1D-to-2D information of molecular input in chemical interaction with a receptor site; and, respectively, the shortest paths in between the endpoints of chemical–biological interaction, as an Euclidian metrics in modeling ligand–receptor space. Moreover, in prediction analysis, the max–min QSAR procedure employs molecular descriptors as electronegativity, chemical hardness, chemical power, electrophilicity, and lipophilicity which associate as well with variational principles of chemical reactivity viz.: mid of HOMO–LUMO annealing, equalization of HOMO–LUMO, minimize of charge flow, potential barrier tunneling and cell walls’ transduction optimization, respectively, for the electrons or molecular ligand–receptor fragments on their highest occupied and lowest unoccupied molecular orbitals (HOMO and LUMO). As a working application the case of anti-HIV pyrimidinic 1,3-disubstituted uracil derivatives is employed towards revealing the chemical reactivity based QSAR optimum mechanism of interaction within sevenfold variational stages as provided by two different SMILES-based screening criteria.


SMILES screening Electronegativity and chemical hardness Chemical power and electrophilicity Lipophilicity QSAR 



Quantitative structure–activity (/property) relationships


Organization for Economic and Cooperation Development


Simplified molecular-input line-entry system


Hard-and-soft acids and bases


Longest SMILES molecular chain


Human immunodeficiency virus


Highly active antiretroviral therapy


Acquired Immune Deficiency Syndrome


Reverse transcriptase


Federal Drug Agency










Ligand–receptor complex


Highest occupied molecular orbital


Lowest unoccupied molecular orbital

log P





Chemical hardness


Chemical power




QSAR endpoint path length



This work was supported by the Romanian National Council of Scientific Research (CNCS-UEFISCDI) through Project TE16/2010-2013 within the PN II-RU-TE-2010-1 framework. Authors thank prof. Adrian Chiriac for incipient discussion and stimulus.


  1. 1.
    Hansch C, Leo A, Hoekman D (1995) Exploring the QSAR hydrophobic, electronic and steric constants. American Chemical Society, Washington, DCGoogle Scholar
  2. 2.
    Patani GA, LaVoie EJ (1996) Bioisosterism: a rational approach in drug design. Chem Rev 96:3147–3176CrossRefGoogle Scholar
  3. 3.
    Dearden JC (2003) In silico prediction of drug toxicity. J Comput Aided Mol Design 17:119–127CrossRefGoogle Scholar
  4. 4.
    Helma C (2005) Predictive toxicology. Taylor & Francis, Washington, DCCrossRefGoogle Scholar
  5. 5.
    Tong W, Hong H, Xie Q, Shi L, Fang H, Perkins R (2005) Assessing QSAR limitations—a regulatory perspective. Curr Comput Aided Drug Design 1:195–205CrossRefGoogle Scholar
  6. 6.
    Leonard JT, Roy K (2006) On selection of training and test sets for the development of predictive QSAR models. QSAR Comb Sci 25:235–251CrossRefGoogle Scholar
  7. 7.
    Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701CrossRefGoogle Scholar
  8. 8.
    Roy K (2007) On some aspects of validation of predictive quantitative structure–activity relationship models. Expert Opin Drug Discov 2:1567–1577CrossRefGoogle Scholar
  9. 9.
    Put R, Vander Heyden Y (2007) Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure–retention relationships. Anal Chim Acta 602:164–172CrossRefGoogle Scholar
  10. 10.
    Roy PP, Leonard JT, Roy K (2008) Exploring the impact of size of training sets for the development of predictive QSAR models. Chemometr Intell Lab Syst 90:31–42CrossRefGoogle Scholar
  11. 11.
    Ajmani S, Jadhav K, Kulkarni SA (2008) Group-based QSAR (G-QSAR): mitigating interpretation challenges in QSAR. QSAR Comb Sci 28:36–51CrossRefGoogle Scholar
  12. 12.
    Roy PP, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660–1701CrossRefGoogle Scholar
  13. 13.
    Manoharan P, Vijayan RSK, Ghoshal N (2010) Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies. J Comput-Aided Mol Design 24:843–864CrossRefGoogle Scholar
  14. 14.
    Chirico N, Gramatica P (2011) Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model 51:2320–2335CrossRefGoogle Scholar
  15. 15.
    Ballante F, Ragno R (2012) 3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications. J Chem Inf Model 52:1674–1685CrossRefGoogle Scholar
  16. 16.
    Brown N (ed) (2012) Bioisosteres in medicinal chemistry. Wiley-VCH, WeinheimGoogle Scholar
  17. 17.
    Putz MV (ed) (2012) QSAR & SPECTRAL-SAR in computational ecotoxicology. Apple Academics & CRC Press, Toronto-New JerseyGoogle Scholar
  18. 18.
    Dirac PAM (1947) The principles of quantum mechanics, 2nd edn. Clarendon Press, OxfordGoogle Scholar
  19. 19.
    Zeilinger A (1999) Experiment and the foundations of quantum physics. Rev Mod Phys 71:S288–S297CrossRefGoogle Scholar
  20. 20.
    Moyer M (2009) Quantum entanglement, photosynthesis and better solar cells. Scientific American. Accessed 23 Feb 2013
  21. 21.
    Scholes G, Collini E, Wong CY, Wilk KE, Curmi PMG, Brumer P, Scholes GD (2010) Coherently wired light-harvesting in photosynthetic marine algae at ambient temperature. Nature 463:644–647CrossRefGoogle Scholar
  22. 22.
    Putz MV, Lacrămă AM (2007) Introducing spectral structure activity relationship (S-SAR) analysis. Application to ecotoxicology. Int J Mol Sci 8:363–391CrossRefGoogle Scholar
  23. 23.
    Lacrămă AM, Putz MV, Ostafe V (2007) A Spectral-SAR model for the anionic–cationic interaction in ionic liquids: application to Vibrio fischeri ecotoxicity. Int J Mol Sci 8:842–863CrossRefGoogle Scholar
  24. 24.
    Chicu SA, Putz MV (2009) Köln–Timişoara molecular activity combined models toward interspecies toxicity assessment. Int J Mol Sci 10:4474–4497CrossRefGoogle Scholar
  25. 25.
    Putz MV, Putz AM, Barou R (2011) Spectral-SAR realization of OECD–QSAR principles. Int J Chem Model 3:173–190Google Scholar
  26. 26.
    Putz AM, Putz MV (2013) Spectral-structure activity relationship (Spectral-SAR) assessment of ionic liquids’ in silico ecotoxicity. In: Kadokawa J (ed) Ionic liquids—new aspects for the future. InTech, RijekaGoogle Scholar
  27. 27.
    Putz MV (2011) Electronegativity and chemical hardness: different patterns in quantum chemistry. Curr Phys Chem 1:111–139CrossRefGoogle Scholar
  28. 28.
    Putz MV (2012) Chemical orthogonal spaces. In: Mathematical Chemistry Monographs, vol 14. Faculty of Science University of Kragujevac, KragujevacGoogle Scholar
  29. 29.
    OECD (2004) Report from the expert group on (quantitative) Structure–activity relationships [(Q)SARs] on the principles for the validation of (Q)SARs, Series on testing and assessment, No. 49, OECD, Paris. Accessed 23 Feb 2013
  30. 30.
    OECD (2007) Guidance Document on the Validation of (Quantitative) structure–activity relationship [(Q)SAR] models, Series on testing and assessment, No. 69, OECD, Paris. Accessed 23 Feb 2013
  31. 31.
    Putz MV, Putz AM (2013) DFT chemical reactivity driven by biological activity: applications for the toxicological fate of chlorinated PAHs. Struct Bond 150:181–232CrossRefGoogle Scholar
  32. 32.
    Maruyama T, Kozai S, Yamasaki T, Witvrouw M, Pannecouque C, Balzarini J, Snoeck R, Andrei G, De Clercq E (2003) Synthesis and antiviral activity of 1,3-disubstituted uracils against HIV-1 and HCMV. Antivir Chem Chemother 14:271–279Google Scholar
  33. 33.
    Garg R, Gupta SP, Gao H, Babu MS, Debnath AK, Hansch C (1999) QSAR studies on anti HIV-1 drugs. Chem Rev 99:3525–3601CrossRefGoogle Scholar
  34. 34.
    Mehellou Y, De Clercq E (2010) Twenty-six years of anti-HIV drug discovery: where do we stand and where do we go? J Med Chem 53:521–538CrossRefGoogle Scholar
  35. 35.
    Esposito F, Corona A, Tramontano E (2012) HIV-1 reverse transcriptase still remains a new drug target: structure, function, classical inhibitors, and new inhibitors with innovative mechanisms of actions. Mol Biol Int. doi: 10.1155/2012/586401
  36. 36.
    Quashie PK, Sloan RD, Wainberg MA (2012) Novel therapeutic strategies targeting HIV integrase. BMC Med 10:34CrossRefGoogle Scholar
  37. 37.
    Kaufmann GR, Cooper DA (2000) Antiretroviral therapy of HIV-1 infection: established treatment strategies and new therapeutic options. Curr Opin Microbiol 3:508–514CrossRefGoogle Scholar
  38. 38.
    De Clercq E (2009) Anti-HIV drugs: 25 compounds approved within 25 years after the discovery of HIV. Int J Antimicrob Agents 33:307–320CrossRefGoogle Scholar
  39. 39.
    Krausslich HG, Bartenschlager R (eds) (2010) Antiviral strategies. Handbook of experimental pharmacology, vol 189. Springer, BerlinGoogle Scholar
  40. 40.
    Heng JB, Ho C, Kim T, Timp R, Aksimentiev A, Grinkova YV, Sligar S, Schulten K, Timp G (2004) Sizing DNA using a nanometer-diameter pore. Biophys J 87:2905–2911CrossRefGoogle Scholar
  41. 41.
    Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36CrossRefGoogle Scholar
  42. 42.
    Weininger D, Weininger A, Weininger JL (1989) SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inf Comput Sci 29:97–101CrossRefGoogle Scholar
  43. 43.
    Ertl P (2010) Molecular structure input on the web. J Cheminform doi: 10.1186/1758-2946-2-1
  44. 44.
    Drefahl A (2011) CurlySMILES: a chemical language to customize and annotate encodings of molecular and nanodevice structures. J Cheminform. doi: 10.1186/1758-2946-3-1 Google Scholar
  45. 45.
    Chemical Identifier Resolver beta 4 (2013) Accessed 23 Feb
  46. 46.
    Hypercube, Inc. (2002) HyperChem 7.01 (Program Package). Hypercube, Inc., GainesvilleGoogle Scholar
  47. 47.
    Topliss JG, Costello JD (1972) Chance correlation in structure–activity studies using multiple regression analysis. J Med Chem 15:1066–1069CrossRefGoogle Scholar
  48. 48.
    Iczkowski RP, Margrave JL (1961) Electronegativity. J Am Chem Soc 83:3547–3551CrossRefGoogle Scholar
  49. 49.
    Parr RG, Donnelly RA, Levy M, Palke WE (1978) Electronegativity: the density functional viewpoint. J Chem Phys 68:3801–3808CrossRefGoogle Scholar
  50. 50.
    Putz MV (2006) Systematic formulation for electronegativity and hardness and their atomic scales within density functional softness theory. Int J Quantum Chem 106:361–389CrossRefGoogle Scholar
  51. 51.
    Putz MV (2009) Electronegativity: quantum observable. Int J Quantum Chem 109:733–738CrossRefGoogle Scholar
  52. 52.
    Putz MV, Russo N, Sicilia E (2005) About the Mulliken electronegativity in DFT. Theor Chem Acc 114:38–45CrossRefGoogle Scholar
  53. 53.
    Parr RG, Pearson RG (1983) Absolute hardness: companion parameter to absolute electronegativity. J Am Chem Soc 105:7512–7516CrossRefGoogle Scholar
  54. 54.
    Pearson RG (1985) Absolute electronegativity and absolute hardness of Lewis acids and bases. J Am Chem Soc 107:6801–6806CrossRefGoogle Scholar
  55. 55.
    Pearson RG (1997) Chemical hardness. Wiley-VCH, WeinheimCrossRefGoogle Scholar
  56. 56.
    Putz MV (2008) Absolute and chemical electronegativity and hardness. Nova Science Publishers Inc., New YorkGoogle Scholar
  57. 57.
    Putz MV, Russo N, Sicilia E (2004) On the application of the HSAB principle through the use of improved computational schemes for chemical hardness evaluation. J Comput Chem 25:994–1003CrossRefGoogle Scholar
  58. 58.
    Putz MV (2011) Chemical action concept and principle. MATCH Commun Math Comput Chem 66:35–63Google Scholar
  59. 59.
    Putz MV (2009) Chemical action and chemical bonding. J Mol Struct (THEOCHEM) 900:64–70CrossRefGoogle Scholar
  60. 60.
    Sanderson RT (1988) Principles of electronegativity Part I. General nature. J Chem Educ 65:112–119CrossRefGoogle Scholar
  61. 61.
    Mortier WJ, Genechten Kv, Gasteiger J (1985) Electronegativity equalization: application and parametrization. J Am Chem Soc 107:829–835CrossRefGoogle Scholar
  62. 62.
    Tachibana A, Parr RG (1992) On the redistribution of electrons for chemical reaction systems. Int J Quantum Chem 41:527–555CrossRefGoogle Scholar
  63. 63.
    Putz MV (2012) Quantum theory: density, condensation, and bonding. Apple Academics, Ontario-New JerseyGoogle Scholar
  64. 64.
    Chattaraj PK, Lee H, Parr RG (1991) Principle of maximum hardness. J Am Chem Soc 113:1854–1855CrossRefGoogle Scholar
  65. 65.
    Chattaraj PK, Liu GH, Parr RG (1995) The maximum hardness principle in the Gyftpoulos–Hatsopoulos three-level model for an atomic or molecular species and its positive and negative ions. Chem Phys Lett 237:171–176CrossRefGoogle Scholar
  66. 66.
    Ayers PW, Parr RG (2000) Variational principles for describing chemical reactions: the Fukui function and chemical hardness revisited. J Am Chem Soc 122:2010–2018CrossRefGoogle Scholar
  67. 67.
    Putz MV (2008) Maximum hardness index of quantum acid-base bonding. MATCH Commun Math Comput Chem 60:845–868Google Scholar
  68. 68.
    Pearson RG (1973) Hard and soft acids and bases. Dowden, Hutchinson & Ross, StroudsbergGoogle Scholar
  69. 69.
    Pearson RG (1990) Hard and soft acids and bases—the evolution of a chemical concept. Coord Chem Rev 100:403–425CrossRefGoogle Scholar
  70. 70.
    Drago RS, Kabler RA (1972) Quantitative evaluation of the HSAB [hard–soft acid–base] concept. Inorg Chem 11:3144–3145CrossRefGoogle Scholar
  71. 71.
    Pearson RG (1972) [Quantitative evaluation of the HSAB (hard–soft acid–base) concept]. Reply to the paper by Drago and Kabler. Inorg Chem 11:3146CrossRefGoogle Scholar
  72. 72.
    Olah GA, Germain A, Lin HC, Forsyth DA (1975) Electrophilic reactions at single bonds. XVIII. Indication of protosolvated de facto substituting agents in the reactions of alkanes with acetylium and nitronium ions in superacidic media. J Am Chem Soc 97:2928–2929CrossRefGoogle Scholar
  73. 73.
    Parr RG, Szentpaly Lv, Liu S (1999) Electrophilicity index. J Am Chem Soc 121:1922–1924CrossRefGoogle Scholar
  74. 74.
    Chattaraj PK, Sarkar U, Roy DR (2006) Electrophilicity index. Chem Rev 106:2065–2091CrossRefGoogle Scholar
  75. 75.
    De Vleeschouwer F, Speybroeck Vv, Waroquier M, Geerlings P, De Proft F (2007) Electrophilicity and nucleophilicity index for radicals. Org Lett 9:2721–2724CrossRefGoogle Scholar
  76. 76.
    Sai KKS, Gilbert TM, Douglas AK (2007) Knorr cyclizations and distonic superelectrophiles. J Org Chem 72:9761–9764CrossRefGoogle Scholar
  77. 77.
    Leo A, Hansch C, Elkins D (1971) Partition coefficients and their uses. Chem Rev 71:525–616CrossRefGoogle Scholar
  78. 78.
    Kubinyi H (1979) Nonlinear dependence of biological activity on hydrophobic character: the bilinear model. Ill Farmaco [Sci] 34:248–276Google Scholar
  79. 79.
    Ghose AK, Crippen GM (1986) Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure–activity relationships I. Partition coefficients as a measure of hydrophobicity. J Comp Chem 7:565–577CrossRefGoogle Scholar
  80. 80.
    Pliska V, Testa B, De Waterbeemd Hv (1996) Lipophilicity in drug action and toxicology. Wiley, New YorkCrossRefGoogle Scholar
  81. 81.
    Ghose AK, Viswanadhan VN, Wendoloski JJ (1998) Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: an analysis of AlogP and ClogP methods. J Phys Chem A 102:3762–3772CrossRefGoogle Scholar
  82. 82.
    Cronin DMT (2006) The role of hydrophobicity in toxicity prediction. Curr Comput Aided Drug Design 2:405–413CrossRefGoogle Scholar
  83. 83.
    Leeson PD, Springthorpe B (2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6:881–890CrossRefGoogle Scholar
  84. 84.
    Putz MV (2011) Quantum parabolic effects of electronegativity and chemical hardness on carbon π-systems. In: Putz MV (ed) Carbon bonding and structures: advances in physics and chemistry. Springer, LondonCrossRefGoogle Scholar
  85. 85.
    Walker LM, Phogat SK, Chan-Hui PY, Wagner D, Phung P, Goss JL, Wrin T, Simek MD, Fling S, Mitcham JL, Lehrman JK, Priddy FH, Olsen OA, Frey SM, Hammond PW, Protocol G Principal Investigators, Kaminsky S, Zamb T, Moyle M, Koff WC, Poignard P, Burton DR (2009) Broad and potent neutralizing antibodies from an african donor reveal a new HIV-1 vaccine target. Science 326:285–289Google Scholar
  86. 86.
    Resource for Biocomputing, Visualization, and Informatics (RBVI) Accessed 23 Feb 2013
  87. 87.
    Halliday D, Resnick R, Krane KS (1992) Physics. Wiley, New YorkGoogle Scholar
  88. 88.
    Griffiths DJ (1999) Introduction to electrodynamics. Prentice Hall, Upper Saddle RiverGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Laboratory of Structural and Computational Chemistry, Biology–Chemistry DepartmentWest University of TimişoaraTimisoaraRomania

Personalised recommendations