Skip to main content

Advertisement

SpringerLink
Log in

Application of electron conformational–genetic algorithm approach to 1,4-dihydropyridines as calcium channel antagonists: pharmacophore identification and bioactivity prediction

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

Two different approaches, namely the electron conformational and genetic algorithm methods (EC-GA), were combined to identify a pharmacophore group and to predict the antagonist activity of 1,4-dihydropyridines (known calcium channel antagonists) from molecular structure descriptors. To identify the pharmacophore, electron conformational matrices of congruity (ECMC)—which include atomic charges as diagonal elements and bond orders and interatomic distances as off-diagonal elements—were arranged for all compounds. The ECMC of the compound with the highest activity was chosen as a template and compared with the ECMCs of other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA) that refers to the pharmacophore. The genetic algorithm was employed to search for the best subset of parameter combinations that contributes the most to activity. Applying the model with the optimum 10 parameters to training (50 compounds) and test (22 compounds) sets gave satisfactory results (\( R_{training}^2 \)= 0.848, \( R_{test}^2 \)= 0.904, with a cross-validated q 2 = 0.780).

Electron conformational and genetic algorithm (EC-GA) methods were combined to identify the pharmacophore group and predict the antagonist activity of 1,4-dihydropyridines (known calcium channel antagonists) from molecular structure descriptors

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
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Itai A, Tomioka N, Kato K (1995) In: Fujita T (ed) QSAR and drug design: new developments and applications. Elsevier, Amsterdam

    Google Scholar 

  2. Thomas G (2003) Fundamentals of medicinal chemistry. Wiley-Blackwell, Weinheim

    Google Scholar 

  3. Şafak C, Şimşek R (2006) Fused 1,4-dihydropyridines as potential calcium modulatory compounds. Mini Rev Med Chem 6:747–755

    Article  Google Scholar 

  4. Fossheim R (1986) Crystal structure of the dihydropyridine Ca2+ antagonist felodipine. Dihydropyridine binding prerequisites assessed from crystallographic data. J Med Chem 29:305–307

    Article  CAS  Google Scholar 

  5. Triggle DJ, Langs DA, Janis RA (1989) Ca2+ channel ligands: structure-function relationships of the 1,4-dihydropyridines. Med Res Rev 9:123–180

    Article  CAS  Google Scholar 

  6. Jiang JL, Li AH, Jang SY, Chang L, Melman N, Moro S, Ji X, Lobkovsky EB, Clardy JC, Jacobson KA (1999) Chiral resolution and stereospecificity of 6-phenyl-4-phenylethynyl-1,4-dihydropyridines as selective A3 adenosine receptor antagonists. J Med Chem 42:3055–3065

    Article  CAS  Google Scholar 

  7. Rhee AM, Jiang JL, Melman N, Olah ME, Stiles GL, Jacobson KA (1996) Interaction of 1,4-dihydropyridine and pyridine derivatives with adenosine receptors: selectivity for A3 receptors. J Med Chem 39:2980–2989

    Article  Google Scholar 

  8. Triggle DJ (2003) 1,4-Dihydropyridines as calcium channel ligands and privileged structures. Cell Mol Neurobiol 23:293–303

    Article  CAS  Google Scholar 

  9. Kahraman P, Turkay M (2007) Classification of 1,4-dihydropyridine calcium channel antagonists using the hyperbox approach. Ind Eng Chem Res 46:4921–4929

    Article  CAS  Google Scholar 

  10. Mohajeri A, Hemmateenejad B, Mehdipour A, Miri R (2008) Modeling calcium channel antagonistic activity of dihydropyridine derivatives using QTMS indices analyzed by GA-PLS and PC-GA-PLS. J Mol Graph Model 26:1057–1065

    Article  CAS  Google Scholar 

  11. Yao X, Liu H, Zhang R, Liu M, Hu Z, Panaye A, Doucet JP, Fan B (2005) QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines. Mol Pharm 2:348–356

    Article  CAS  Google Scholar 

  12. Takahata Y, Costa MCA, Gaudio AC (2003) Comparison between neural networks (NN) and principal component analysis (PCA): structure activity relationships of 1,4-dihydropyridine calcium channel antagonists (nifedipine analogues). J Chem Inf Comput Sci 43:540–544

    Article  CAS  Google Scholar 

  13. Schleifer KJ, Tot E (2002) CoMFA, CoMSIA and GRID/GOLPE studies on calcium entry blocking 1,4-dihydropryridines. Quant Struct Act Relat 21:239–248

    Article  CAS  Google Scholar 

  14. Safarpour MA, Hemmateenejad B, Miri R, Jamali M (2003) Quantum chemical-QSAR study of some newly synthesized 1,4-dihydropyridine calcium channel blockers. QSAR Comb Sci 22:997–1005

    Article  Google Scholar 

  15. Niculescu SP (2003) Artificial neural networks and genetic algorithms in QSAR. J Mol Struct THEOCHEM 622:71–83

    Article  CAS  Google Scholar 

  16. Cronin MTD, Schultz TW (2003) Pitfalls in quantitative structure–activity relationships (QSARs) for predicting toxicity. J Mol Struct THEOCHEM 622:39–52

    Article  CAS  Google Scholar 

  17. Schultz TW, Cronin MTD (2003) Essential and desirable characteristics of ecotoxicity QSARs. Environ Toxicology Chem 22:599–607

    CAS  Google Scholar 

  18. Kubinyi H, Folkers G, Martin YC (eds) (1998) 3D QSAR in drug design: recent advances. Kluwer, Dordrecht

    Google Scholar 

  19. Hopfinger AJ, Wang S, Tokarski JS, Jin B, Albuquerque M, Madhav PJ, Duraiswami C (1997) Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J Am Chem Soc 119:10509–10524

    Article  CAS  Google Scholar 

  20. Becker OM, Levy Y, Ravitz O (2000) Conformation spaces, and bioactivity. J Phys Chem B 104:2123–2135

    Article  CAS  Google Scholar 

  21. Langer T, Hoffmann RD (eds) (2006) Pharmacophores and pharmacophore searches. Wiley-VCH, Weinheim

    Google Scholar 

  22. Guner OF (2002) History and evolution of the pharmacophore concept in computer-aided drug design. Curr Top Med Chem 2:1321–1332

    Article  CAS  Google Scholar 

  23. Dimoglu AS, Vlad PF, Shvets NM, Coltsa MN, Guzel Y, Saracoglu M, Saripinar E, Patat S (1995) Electronic-topological investigations of the relationships between chemical structure and ambergris odor. New J Chem 19:1217–1226

    Google Scholar 

  24. Saripinar E, Guzel Y, Patat S, Yildirim I, Akcamur Y, Dimoglo A (1996) Electron-topological investigation of the structure–antitubercular activity relationship of thiosemicarbazone derivatives. Arzneim Forsch (Drug Res) 46:824–828

    CAS  Google Scholar 

  25. Guzel Y, Saripinar E, Yildirim I (1997) Electron-toplogical (ET) investigation of structure–antagonist activity of a series of dibenzo[a,d]cycloalkenimines. J Mol Struct THEOCHEM 418:83–91

    Article  Google Scholar 

  26. Rosines E, Bersuker IB, Boggs JE (2001) Pharmacophore identification and bioactivity prediction for group I metabotropic glutamate receptor agonists by the electron-conformational QSAR method. Quant Struct Act Relat 20:327–333

    Article  CAS  Google Scholar 

  27. Makkouk Al H, Bersuker IB, Boggs JE (2004) Quantitative drug activity prediction for inhibitors of human breast carcinoma. Int J Pharm Med 18:81–89

    Article  Google Scholar 

  28. Marenich AV, Yong PH, Bersuker IB, Boggs JE (2008) Quantitative antidiabetic activity prediction for the class of guanidino- and aminoguanidinopropionic acid analogs based on electron-conformational studies. J Chem Inf Model 48:556–568

    Article  CAS  Google Scholar 

  29. Bersuker IB, Bahceci S, Boggs JE, Pearlman RS (1999) A novel electron-conformational approach to molecular modeling for QSAR by identification of pharmacophore and anti-pharmacophore shielding. SAR QSAR Environ Res 10:157–173

    Article  CAS  Google Scholar 

  30. Bersuker IB (2008) QSAR without arbitrary descriptors: the electron-conformational method. J Comput Aided Mol Des 22:423–430

    Article  CAS  Google Scholar 

  31. Eriksson L, Andersson PL, Johansson E, Tysklind M (2006) Megavariate analysis of environmental QSAR data. Part I. A basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD). Mol Divers 10:169–186

    Article  CAS  Google Scholar 

  32. Dudek AZ, Arodz T, Galvez J (2006) Computational methods in developing quantitative structure–activity relationships (QSAR): a review. Comb Chem High Throughput Screening 9:213–228

    Article  CAS  Google Scholar 

  33. Holland JH (1975) Adaptation in artificial and natural systems. University of Michigan, Ann Arbor

    Google Scholar 

  34. Terfloth L, Gasteiger J (2001) Neural networks and genetic algorithms in drug design. DDT 6:102–108

    Google Scholar 

  35. Verma A, Llora X, Venkataraman S, Goldberg DE, Campbell RH (2010) Scaling eCGA model building via data-ıntensive computing. In: WCCI 2010 IEEE World Congr on Computational Intelligence, Barcelona, Spain, 18–23 July 2010

  36. Jones G (2010) GAPE: an improved genetic algorithm for pharmacophore elucidation. J Chem Inf Model 50:2001–2018

    Article  CAS  Google Scholar 

  37. Reddy AS, Kumar S, Garg R (2010) Hybrid-genetic algorithm based descriptor optimization and QSAR models for predicting the biological activity of tipranavir analogs for HIV protease inhibition. J Mol Graph Model 28:852–862

    Article  CAS  Google Scholar 

  38. Mercader AG, Duchowicz PR, Fernandez FM, Castro EA (2010) Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines. J Chem Inf Model 50:1542–1548

    Article  CAS  Google Scholar 

  39. Guha R, Jurs PC (2005) Interpreting computational neural network QSAR models: a measure of descriptor importance. J Chem Inf Model 45:800–806

    Article  CAS  Google Scholar 

  40. Sarıpınar E, Geçen N, Sahin K, Yanmaz E (2010) Pharmacophore identification and bioactivity prediction for triaminotriazine derivatives by electron conformational-genetic algorithm QSAR method. Eur J Med Chem 45:4157–4168

    Google Scholar 

  41. Sahin K, Sarıpınar E, Yanmaz E, Geçen N (2011) Quantitative bioactivity prediction and pharmacophore identification for benzotriazine derivatives using the electron conformational-genetic algorithm in QSAR. SAR and QSAR Environ Res. doi:10.1080/1062936X.2010.548341

  42. Yanmaz E, Sarıpınar E, Sahin K, Geçen N, Çopur F (2011) 4D-QSAR analysis and pharmacophore modeling: Electron conformational-genetic algorithm approach for penicillins. Bioorg Med Chem. doi:10.1016/j.bmc.2011.02.035

    Google Scholar 

  43. Shafiee A, Miri R, Dehpour AR, Soleymani F (1996) Synthesis and calcium-channel antagonist activity of nifedipine analogues containing nitroimidazolyl substituent in guinea-pig ileal smooth muscle. Pharmaceut Sci 2:541–543

    CAS  Google Scholar 

  44. Miri R, Howlett SE, Knaus EE (1997) Synthesis and calcium channel modulating effects of isopropyl 1,4-dihydro-2,6-dimethyl-3-nitro-4-(thienyl)-5-pyridinecarboxylates. Arch Pharm Pharm Med Chem 330:290–294

    Article  CAS  Google Scholar 

  45. Miri R, McEwen CA, Knaus EE (2000) Synthesis and calcium channel modulating effects of modified Hantzsch nitrooxyalkyl 1,4-dihydro-2,6-dimethyl-3-nitro-4-(pyridinyl or 2-trifluoromethylphenyl)-5-pyridinecarboxylates. Drug Dev Res 51:225–232

    Article  CAS  Google Scholar 

  46. Miri R, Dehpour AR, Azimi M, Shafiee A (2001) Synthesis and smooth muscle calcium channel antagonist effect of alkyl, aminoalkyl-1,4-dihydro-2,6-dimethyl-4-nitroimidazole-3,5-pyridine dicarboxylates. J School Pharmacy Med Sci Univ Tehran 9:40–45

    CAS  Google Scholar 

  47. Miri R, Niknahad H, Vesal G, Shafiee A (2002) Synthesis and calcium channel antagonist activities of 3-nitrooxyalkyl, 5-alkyl-1,4-dihydro-2,6-dimethyl-4-(1-methyl-5-nitro-2-imidazolyl)-3,5-pyridinedicarboxylates. Il Farmaco 57:123–128

    Article  CAS  Google Scholar 

  48. Wavefunction, Inc. (2006) SPARTAN, v.06. Wavefunction, Inc., Irvine

  49. Bersuker IB (2003) Pharmacophore identification and quantitative bioactivity prediction using the electron-conformational method. Curr Pharm Des 9:1575–1606

    Article  CAS  Google Scholar 

  50. Bersuker IB, Dimoglo AS (1991) The electron-topological approach to the QSAR problem. In: Lipkowitz KB, Boyd DB (eds) Reviews in computational chemistry, 2nd edn. Wiley, New York, pp 423–460

    Chapter  Google Scholar 

  51. Bersuker IB, Bahceci S, Boggs JE, Pearlman RS (1999) An electron-conformational method of identification of pharmacophore and anti-pharmacophore shielding: application to rice blast activity. J Comput Aided Mol Des 13:419–434

    Article  CAS  Google Scholar 

  52. Dimoglo AS, Shvets NM, Tetko IV, Livingstone DJ (2001) Electronic-topological investigation of the structure–acetylcholinesterase inhibitor activity relationship in the series of n-benzylpiperidine derivatives. Quant Struct Act Relat 20:31–45

    Article  CAS  Google Scholar 

  53. Pavlov T, Todorov M, Stoyanova G, Schmieder P, Aladjov H, Serafimova R, Mekenyan O (2007) Conformational coverage by a genetic algorithm: saturation of conformational space. J Chem Inf Model 47:851–863

    Article  CAS  Google Scholar 

  54. Consonni V, Ballabio D, Todeschini R (2009) Comments on the definition of the Q2 parameter for QSAR validation. J Chem Inf Model 49:1669–1678

    Article  CAS  Google Scholar 

  55. Damme SV, Bultınck P (2007) Software news and update a new computer program for QSAR-analysis: ARTE-QSAR. J Comput Chem 28:1924–1928

    Article  Google Scholar 

  56. Schuurmann G, Ebert RU, Chen J, Wang B, Kuhne R (2008) External validation and prediction employing the predictive squared correlation coefficients test set activity mean vs training set activity mean. J Chem Inf Model 48:2140–2145

    Article  Google Scholar 

  57. Topliss JG, Edwards RP (1979) Chance factors in studies of quantitative structure–activity relationships. J Med Chem 22:1238–1244

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  59. Chattaraj PK, Maiti B (2001) Reactivity dynamics in atom–field interactions: a quantum fluid density functional study. J Phys Chem A 105:169–183

    Article  CAS  Google Scholar 

  60. Vleeschouwer FD, Speybroeck VV, Waroquier M, Geerlings P, Proft FD (2007) Electrophilicity and nucleophilicity index for radicals. Org Lett 9:2721–2724

    Article  Google Scholar 

  61. Cramer CJ, Famini G, Lowrey AH (1993) Use of calculated quantum chemical properties as surrogates for solvatochromic parameters in structure–activity relationships. Acc Chem Res 26:599–605

    Article  CAS  Google Scholar 

  62. Oliferenko AA, Oliferenko PV, Huddleston JG, Rogers RD, Palyulin VA, Zefirov NS, Katritzky AR (2004) Theoretical scales of hydrogen bond acidity and basicity for application in QSAR/QSPR studies and drug design. Partitioning of aliphatic compounds. J Chem Inf Comput Sci 44:1042–1055

    Article  CAS  Google Scholar 

  63. Patel DM, Patel NM (2009) QSAR analysis of aminoquinoline analogues as MCH1 receptor antagonist. J Sci Res 1:594–605

    Article  CAS  Google Scholar 

  64. Livingstone D (1995) Data analysis for chemists. Oxford University Press, New York

    Google Scholar 

  65. Wold S (1978) Cross-validatory estimation of the number of components in factor and principal components models. Technometrics 20:397–405

    Article  Google Scholar 

  66. Cramer R, Patterson D, Bunce J (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967

    Article  CAS  Google Scholar 

  67. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146

    Article  CAS  Google Scholar 

  68. Yang GF, Huang X (2006) Development of quantitative structure–activity relationships and its application in rational drug design. Curr Pharm Des 12:4601–4611

    Article  CAS  Google Scholar 

  69. Fox T, Kriegl JM (2006) Machine learning techniques for in silico modeling of drug metabolism. Curr Top Med Chem 6:1579–1591

    Article  CAS  Google Scholar 

  70. Helma C, Kazius J (2006) Artificial intelligence and data mining for toxicity prediction. Curr Comput Aided Drug Des 2:123–133

    Article  CAS  Google Scholar 

Download references

Acknowledgement

This project was financially supported by the Scientific Technical Research Council of Turkey (TUBITAK, Grant No. 107T385) and the Research Fund of Erciyes University (BAP, Project Number: FBD-09-928). The authors are grateful to Mustafa Yıldırım, Fatih Çopur and Serkan Şahin for their valuable suggestions.

Author information

Authors and Affiliations

  1. Department of Chemistry, Science and Art Faculty, Siirt Universty, Siirt, 56100, Turkey

    Nazmiye Geçen

  2. Department of Chemistry, Science Faculty, Erciyes University, 38039, Kayseri, Turkey

    Emin Sarıpınar

  3. Department of Chemistry, Altınoluk Vocational College, Balıkesir University, Balıkesir, Turkey

    Ersin Yanmaz

  4. Faculty of Art and Science, Bitlis Eren University, Bitlis, Turkey

    Kader Şahin

Authors
  1. Nazmiye Geçen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emin Sarıpınar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ersin Yanmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kader Şahin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emin Sarıpınar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Geçen, N., Sarıpınar, E., Yanmaz, E. et al. Application of electron conformational–genetic algorithm approach to 1,4-dihydropyridines as calcium channel antagonists: pharmacophore identification and bioactivity prediction. J Mol Model 18, 65–82 (2012). https://doi.org/10.1007/s00894-011-1024-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00894-011-1024-5

Keywords

Search

Navigation