Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Research
  • Published:
Structural Chemistry Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Abbreviations

(Q)SA(/P)R:

Quantitative structure–activity (/property) relationships

OECD:

Organization for Economic and Cooperation Development

SMILES:

Simplified molecular-input line-entry system

HSAB:

Hard-and-soft acids and bases

LoSMoC:

Longest SMILES molecular chain

HIV:

Human immunodeficiency virus

HAART:

Highly active antiretroviral therapy

AIDS:

Acquired Immune Deficiency Syndrome

RT:

Reverse transcriptase

FDA:

Federal Drug Agency

CB:

Chemical–biological

A:

Activity

L:

Ligand

R:

Receptor

LR:

Ligand–receptor complex

HOMO:

Highest occupied molecular orbital

LUMO:

Lowest unoccupied molecular orbital

log P :

Lipophilicity

χ :

Electronegativity

η :

Chemical hardness

π :

Chemical power

ω :

Electrophilicity

δ :

QSAR endpoint path length

References

  1. Hansch C, Leo A, Hoekman D (1995) Exploring the QSAR hydrophobic, electronic and steric constants. American Chemical Society, Washington, DC

    Google Scholar 

  2. Patani GA, LaVoie EJ (1996) Bioisosterism: a rational approach in drug design. Chem Rev 96:3147–3176

    Article  CAS  Google Scholar 

  3. Dearden JC (2003) In silico prediction of drug toxicity. J Comput Aided Mol Design 17:119–127

    Article  CAS  Google Scholar 

  4. Helma C (2005) Predictive toxicology. Taylor & Francis, Washington, DC

    Book  Google Scholar 

  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 1:195–205

    Article  CAS  Google Scholar 

  6. Leonard JT, Roy K (2006) On selection of training and test sets for the development of predictive QSAR models. QSAR Comb Sci 25:235–251

    Article  CAS  Google Scholar 

  7. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701

    Article  CAS  Google Scholar 

  8. Roy K (2007) On some aspects of validation of predictive quantitative structure–activity relationship models. Expert Opin Drug Discov 2:1567–1577

    Article  CAS  Google Scholar 

  9. Put R, Vander Heyden Y (2007) Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure–retention relationships. Anal Chim Acta 602:164–172

    Article  CAS  Google Scholar 

  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 90:31–42

    Article  CAS  Google Scholar 

  11. Ajmani S, Jadhav K, Kulkarni SA (2008) Group-based QSAR (G-QSAR): mitigating interpretation challenges in QSAR. QSAR Comb Sci 28:36–51

    Article  Google Scholar 

  12. Roy PP, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660–1701

    Article  CAS  Google Scholar 

  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 24:843–864

    Article  CAS  Google Scholar 

  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 51:2320–2335

    Article  CAS  Google Scholar 

  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 52:1674–1685

    Article  CAS  Google Scholar 

  16. Brown N (ed) (2012) Bioisosteres in medicinal chemistry. Wiley-VCH, Weinheim

    Google Scholar 

  17. Putz MV (ed) (2012) QSAR & SPECTRAL-SAR in computational ecotoxicology. Apple Academics & CRC Press, Toronto-New Jersey

    Google Scholar 

  18. Dirac PAM (1947) The principles of quantum mechanics, 2nd edn. Clarendon Press, Oxford

    Google Scholar 

  19. Zeilinger A (1999) Experiment and the foundations of quantum physics. Rev Mod Phys 71:S288–S297

    Article  CAS  Google Scholar 

  20. Moyer M (2009) Quantum entanglement, photosynthesis and better solar cells. Scientific American. http://www.scientificamerican.com/article.cfm?id=quantum-entanglement-and-photo. Accessed 23 Feb 2013

  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 463:644–647

    Article  Google Scholar 

  22. Putz MV, Lacrămă AM (2007) Introducing spectral structure activity relationship (S-SAR) analysis. Application to ecotoxicology. Int J Mol Sci 8:363–391

    Article  CAS  Google Scholar 

  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 8:842–863

    Article  Google Scholar 

  24. Chicu SA, Putz MV (2009) Köln–Timişoara molecular activity combined models toward interspecies toxicity assessment. Int J Mol Sci 10:4474–4497

    Article  CAS  Google Scholar 

  25. Putz MV, Putz AM, Barou R (2011) Spectral-SAR realization of OECD–QSAR principles. Int J Chem Model 3:173–190

    Google Scholar 

  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, Rijeka

    Google Scholar 

  27. Putz MV (2011) Electronegativity and chemical hardness: different patterns in quantum chemistry. Curr Phys Chem 1:111–139

    Article  CAS  Google Scholar 

  28. Putz MV (2012) Chemical orthogonal spaces. In: Mathematical Chemistry Monographs, vol 14. Faculty of Science University of Kragujevac, Kragujevac

  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. http://www.oecd.org/env/ehs/risk-assessment/guidancedocumentsandreportsrelatedtoqsars.htm. Accessed 23 Feb 2013

  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. http://www.oecd.org/env/ehs/risk-assessment/guidancedocumentsandreportsrelatedtoqsars.htm. Accessed 23 Feb 2013

  31. Putz MV, Putz AM (2013) DFT chemical reactivity driven by biological activity: applications for the toxicological fate of chlorinated PAHs. Struct Bond 150:181–232

    Article  CAS  Google Scholar 

  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 14:271–279

    CAS  Google Scholar 

  33. Garg R, Gupta SP, Gao H, Babu MS, Debnath AK, Hansch C (1999) QSAR studies on anti HIV-1 drugs. Chem Rev 99:3525–3601

    Article  CAS  Google Scholar 

  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 53:521–538

    Article  CAS  Google Scholar 

  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. Quashie PK, Sloan RD, Wainberg MA (2012) Novel therapeutic strategies targeting HIV integrase. BMC Med 10:34

    Article  CAS  Google Scholar 

  37. Kaufmann GR, Cooper DA (2000) Antiretroviral therapy of HIV-1 infection: established treatment strategies and new therapeutic options. Curr Opin Microbiol 3:508–514

    Article  CAS  Google Scholar 

  38. De Clercq E (2009) Anti-HIV drugs: 25 compounds approved within 25 years after the discovery of HIV. Int J Antimicrob Agents 33:307–320

    Article  CAS  Google Scholar 

  39. Krausslich HG, Bartenschlager R (eds) (2010) Antiviral strategies. Handbook of experimental pharmacology, vol 189. Springer, Berlin

  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 87:2905–2911

    Article  CAS  Google Scholar 

  41. Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36

    Article  CAS  Google Scholar 

  42. Weininger D, Weininger A, Weininger JL (1989) SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inf Comput Sci 29:97–101

    Article  CAS  Google Scholar 

  43. Ertl P (2010) Molecular structure input on the web. J Cheminform doi:10.1186/1758-2946-2-1

  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. Chemical Identifier Resolver beta 4 (2013) http://cactus.nci.nih.gov/chemical/structure. Accessed 23 Feb

  46. Hypercube, Inc. (2002) HyperChem 7.01 (Program Package). Hypercube, Inc., Gainesville

  47. Topliss JG, Costello JD (1972) Chance correlation in structure–activity studies using multiple regression analysis. J Med Chem 15:1066–1069

    Article  CAS  Google Scholar 

  48. Iczkowski RP, Margrave JL (1961) Electronegativity. J Am Chem Soc 83:3547–3551

    Article  CAS  Google Scholar 

  49. Parr RG, Donnelly RA, Levy M, Palke WE (1978) Electronegativity: the density functional viewpoint. J Chem Phys 68:3801–3808

    Article  CAS  Google Scholar 

  50. Putz MV (2006) Systematic formulation for electronegativity and hardness and their atomic scales within density functional softness theory. Int J Quantum Chem 106:361–389

    Article  CAS  Google Scholar 

  51. Putz MV (2009) Electronegativity: quantum observable. Int J Quantum Chem 109:733–738

    Article  CAS  Google Scholar 

  52. Putz MV, Russo N, Sicilia E (2005) About the Mulliken electronegativity in DFT. Theor Chem Acc 114:38–45

    Article  CAS  Google Scholar 

  53. Parr RG, Pearson RG (1983) Absolute hardness: companion parameter to absolute electronegativity. J Am Chem Soc 105:7512–7516

    Article  CAS  Google Scholar 

  54. Pearson RG (1985) Absolute electronegativity and absolute hardness of Lewis acids and bases. J Am Chem Soc 107:6801–6806

    Article  CAS  Google Scholar 

  55. Pearson RG (1997) Chemical hardness. Wiley-VCH, Weinheim

    Book  Google Scholar 

  56. Putz MV (2008) Absolute and chemical electronegativity and hardness. Nova Science Publishers Inc., New York

    Google Scholar 

  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 25:994–1003

    Article  CAS  Google Scholar 

  58. Putz MV (2011) Chemical action concept and principle. MATCH Commun Math Comput Chem 66:35–63

    CAS  Google Scholar 

  59. Putz MV (2009) Chemical action and chemical bonding. J Mol Struct (THEOCHEM) 900:64–70

    Article  CAS  Google Scholar 

  60. Sanderson RT (1988) Principles of electronegativity Part I. General nature. J Chem Educ 65:112–119

    Article  CAS  Google Scholar 

  61. Mortier WJ, Genechten Kv, Gasteiger J (1985) Electronegativity equalization: application and parametrization. J Am Chem Soc 107:829–835

    Article  CAS  Google Scholar 

  62. Tachibana A, Parr RG (1992) On the redistribution of electrons for chemical reaction systems. Int J Quantum Chem 41:527–555

    Article  CAS  Google Scholar 

  63. Putz MV (2012) Quantum theory: density, condensation, and bonding. Apple Academics, Ontario-New Jersey

    Google Scholar 

  64. Chattaraj PK, Lee H, Parr RG (1991) Principle of maximum hardness. J Am Chem Soc 113:1854–1855

    Article  Google Scholar 

  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 237:171–176

    Article  CAS  Google Scholar 

  66. Ayers PW, Parr RG (2000) Variational principles for describing chemical reactions: the Fukui function and chemical hardness revisited. J Am Chem Soc 122:2010–2018

    Article  CAS  Google Scholar 

  67. Putz MV (2008) Maximum hardness index of quantum acid-base bonding. MATCH Commun Math Comput Chem 60:845–868

    Google Scholar 

  68. Pearson RG (1973) Hard and soft acids and bases. Dowden, Hutchinson & Ross, Stroudsberg

  69. Pearson RG (1990) Hard and soft acids and bases—the evolution of a chemical concept. Coord Chem Rev 100:403–425

    Article  CAS  Google Scholar 

  70. Drago RS, Kabler RA (1972) Quantitative evaluation of the HSAB [hard–soft acid–base] concept. Inorg Chem 11:3144–3145

    Article  CAS  Google Scholar 

  71. Pearson RG (1972) [Quantitative evaluation of the HSAB (hard–soft acid–base) concept]. Reply to the paper by Drago and Kabler. Inorg Chem 11:3146

    Article  CAS  Google Scholar 

  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 97:2928–2929

    Article  CAS  Google Scholar 

  73. Parr RG, Szentpaly Lv, Liu S (1999) Electrophilicity index. J Am Chem Soc 121:1922–1924

    Article  CAS  Google Scholar 

  74. Chattaraj PK, Sarkar U, Roy DR (2006) Electrophilicity index. Chem Rev 106:2065–2091

    Article  CAS  Google Scholar 

  75. De Vleeschouwer F, Speybroeck Vv, Waroquier M, Geerlings P, De Proft F (2007) Electrophilicity and nucleophilicity index for radicals. Org Lett 9:2721–2724

    Article  Google Scholar 

  76. Sai KKS, Gilbert TM, Douglas AK (2007) Knorr cyclizations and distonic superelectrophiles. J Org Chem 72:9761–9764

    Article  CAS  Google Scholar 

  77. Leo A, Hansch C, Elkins D (1971) Partition coefficients and their uses. Chem Rev 71:525–616

    Article  CAS  Google Scholar 

  78. Kubinyi H (1979) Nonlinear dependence of biological activity on hydrophobic character: the bilinear model. Ill Farmaco [Sci] 34:248–276

    CAS  Google Scholar 

  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 7:565–577

    Article  CAS  Google Scholar 

  80. Pliska V, Testa B, De Waterbeemd Hv (1996) Lipophilicity in drug action and toxicology. Wiley, New York

    Book  Google Scholar 

  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 102:3762–3772

    Article  CAS  Google Scholar 

  82. Cronin DMT (2006) The role of hydrophobicity in toxicity prediction. Curr Comput Aided Drug Design 2:405–413

    Article  CAS  Google Scholar 

  83. Leeson PD, Springthorpe B (2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6:881–890

    Article  CAS  Google Scholar 

  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, London

    Chapter  Google Scholar 

  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 326:285–289

  86. Resource for Biocomputing, Visualization, and Informatics (RBVI) http://www.cgl.ucsf.edu/chimera/data/hiv09/hiv-demo.html. Accessed 23 Feb 2013

  87. Halliday D, Resnick R, Krane KS (1992) Physics. Wiley, New York

    Google Scholar 

  88. Griffiths DJ (1999) Introduction to electrodynamics. Prentice Hall, Upper Saddle River

    Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Laboratory of Structural and Computational Chemistry, Biology–Chemistry Department, West University of Timişoara, Pestalozzi Str. No. 16, Timisoara, 300115, Romania

    Mihai V. Putz & Nicoleta A. Dudaş

Authors
  1. Mihai V. Putz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicoleta A. Dudaş
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mihai V. Putz.

Additional information

This article is a dedication in honor of recently retired premier Spanish calorimetrist Maria Victoria Roux.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Putz, M.V., Dudaş, N.A. Variational principles for mechanistic quantitative structure–activity relationship (QSAR) studies: application on uracil derivatives’ anti-HIV action. Struct Chem 24, 1873–1893 (2013). https://doi.org/10.1007/s11224-013-0249-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11224-013-0249-6

Keywords

Search

Navigation