Skip to main content

Advertisement

SpringerLink
Log in

Design of novel antituberculosis compounds using graph-theoretical and substructural approaches

  • Full Length Paper
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem, two models for the design and prediction of new antituberculosis agents were obtained. The first used a mixed approach, containing descriptors based on fragments and the topological substructural molecular design approach (TOPS-MODE) descriptors. The other model used a combination of two-dimensional (2D) and three-dimensional (3D) descriptors. A data set of 167 compounds with great structural variability, 72 of them antituberculosis agents and 95 compounds belonging to other pharmaceutical categories, was analyzed. The first model showed sensitivity, specificity, and accuracy values above 80% and the second one showed values higher than 75% for these statistical indices. Subsequently, 12 structures of imidazoles not included in this study were designed, taking into account the two models. In both cases accuracy was 100%, showing that the methodology in silico developed by us is promising for the rational design of antituberculosis drugs.

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

Similar content being viewed by others

References

  1. Cohn DL, Bustreo F, Raviglione MC (1997) Drug resistant tuberculosis: review of the worldwide situation and WHO/IUATLD Global Surveillance Project. International Union against Tuberculosis and Lung Disease. Clin Infect Dis 24: 121–130

    Google Scholar 

  2. World Health Organization Report Available at http://www.who.int/tb/en/

  3. Hansch C, Leo A (1995) QSAR: fundamentals and applications in chemistry and biology. American Chemical Society, Washington, DC

    Google Scholar 

  4. Kubinyi H (1993) QSAR: Hansch analysis and related approaches. VCH, New York

    Book  Google Scholar 

  5. Kier LB, Hall LH (1976) Molecular connectivity in chemistry and drug research. Academic, New York

    Google Scholar 

  6. Kier LB, Hall LH (1986) Molecular connectivity in structure-activity analysis. Research Studies, Letchworth, Herts

    Google Scholar 

  7. García-Domenech R, García FJ, Soler RM, Gálvez J, Anton-Fos GM, de Julián-Ortiz JV (1996) New analgesics design by molecular topology. Quant Struc-Act Rel 15: 1–7. doi:10.1002/qsar.19960150102

    Article  Google Scholar 

  8. de Julián-Ortiz JV, Gálvez J, Muñoz-Collado C, García-Domenech R, Gimeno-Cardona C (1999) Virtual combinatorial synthesis and computational screening of new potential anti-herpes compounds. J Med Chem 42: 3308–3314. doi:10.1021/jm981132u

    Article  PubMed  Google Scholar 

  9. de Gregorio Alapont C, García-Domenech R, Gálvez J, Ros MJ, Wolski S, García MD (2000) Molecular topology: a useful tool for the search of new antibacterials. Bioorg Med Chem Lett 10: 2033–2036. doi:10.1016/S0960-894X(00)00406-6

    Article  PubMed  Google Scholar 

  10. Gozalbes R, Brun-Pascaud M, Gálvez J, García-Domenech R, Girard PM, Doucet JP et al (2000) Prediction of quinolone activity against Mycobacterium avium by molecular topology and virtual computational screening. Antimicrob Agents Chemother 44: 2764–2770. doi:10.1128/AAC.44.10.2764-2770.2000

    Article  PubMed  CAS  Google Scholar 

  11. Kubinyi H, Kehrhahn OH (1976) Quantitative structure-activity relationships. 1. The modified Free-Wilson approach. J Med Chem 19: 578–586. doi:10.1021/jm00227a003

    Article  PubMed  CAS  Google Scholar 

  12. Kubinyi H (1976) Quantitative structure-activity relationships. 2. A mixed approach, based on Hansch and Free-Wilson analysis. J Med Chem 19: 587–600. doi:10.1021/jm00227a004

    Article  PubMed  CAS  Google Scholar 

  13. Kiritsy JA, Yung DK, Mahony DE (1978) Synthesis and quantitative structure-activity relationships of some antibacterial 3-formylrifamycin SV N-(4-substituted phenyl)piperazinoacethydrazones. J Med Chem 21: 1301–1307. doi:10.1021/jm00210a025

    Article  PubMed  CAS  Google Scholar 

  14. Pasqualoto KF, Ferreira EI, Santos-Filho OA, Hopfinger AJ (2004) Rational design of new antituberculosis agents: receptor-independent four-dimensional quantitative structure-activity relationship analysis of a set of isoniazid derivatives. J Med Chem 47: 3755–3764. doi:10.1021/jm049913k

    Article  PubMed  CAS  Google Scholar 

  15. Desai B, Sureja D, Naliapara Y, Shah A, Saxena AK (2001) Synthesis and QSAR studies of 4-substituted phenyl-2,6-dimethyl-3, 5-bis-N-(substituted phenyl)carbamoyl-1,4-dihydropyridines as potential antitubercular agents. Bioorg Med Chem 9: 1993–1998. doi:10.1016/S0968-0896(01)00141-9

    Article  PubMed  CAS  Google Scholar 

  16. Gallegos A, Carbo-Dorca R, Ponec R, Waisser K (2004) Similarity approach to QSAR: application to antimycobacterial benzoxazines. Int J Pharm 269: 51–60. doi:10.1016/j.ijpharm.2003.08.013

    Article  PubMed  CAS  Google Scholar 

  17. Ragno R, Marshall GR, Di Santo R, Costi R, Massa S, Rompei R et al (2000) Antimycobacterial pyrroles: synthesis, anti-Mycobactrium tuberculosis activity and QSAR studies. Bioorg Med Chem 8: 1423–1432. doi:10.1016/S0968-0896(00)00061-4

    Article  PubMed  CAS  Google Scholar 

  18. Suling WJ, Maddry JA (2001) Antimycobacterial activity of 1-deaza-7,8-dihydropteridine derivatives against Mycobacterium tuberculosis and Mycobacterium avium complex in vitro. J Antimicrob Chemother 47: 451–454. doi:10.1093/jac/47.4.451

    Article  PubMed  CAS  Google Scholar 

  19. Besalu E, Ponec R, de Julián-Ortiz JV (2003) Virtual generation of agents against Mycobacterium tuberculosis. Mol Divers 6: 107–120. doi:10.1023/B:MODI.0000006839.52374.d7

    Article  PubMed  CAS  Google Scholar 

  20. García-García A, Galvez J, de Julián-Ortiz JV, García-Domenech R, Muños C, Guna R et al (2005) Search of chemical scaffolds for novel antituberculosis agents. J Biomol Screen 10: 206–214. doi:10.1177/1087057104273486

    Article  PubMed  Google Scholar 

  21. Estrada E, Molina E (2001) Novel local (fragment-based) topological molecular descriptors for QSPR/QSAR and molecular design. J Mol Graph Model 20: 54–64. doi:10.1016/S1093-3263(01)00100-0

    Article  PubMed  CAS  Google Scholar 

  22. Viswanadhan VN, Ghose AK, Revankar GR, Robins RK (1989) Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J Chem Inf Comput Sci 29: 163–172. doi:10.1021/ci00063a006

    CAS  Google Scholar 

  23. Viswanadhan VN, Reddy MR, Bacquet RJ, Erion MD (1993) Assessment of methods used for predicting lipophilicity: application to nucleosides and nucleoside bases. J Comput Chem 14: 1019–1026. doi:10.1002/jcc.540140903

    Article  CAS  Google Scholar 

  24. Zagrovic B, van Gunsteren WF (2007) Computational analysis of the mechanism and thermodynamics of inhibition of phophodiesterase 5A by synthetic ligands. J Chem Theory Comput 3: 301–311. doi:10.1021/ct600322d

    Article  CAS  Google Scholar 

  25. Bren U, Martínek V, Florián J (2006) Free energy simulations of uncatalized DNA replication fidelity: structure and stability of T·G and dTTP· G terminal DNA mismatches flanked by a single dangling nucleotide. J Phys Chem B 110: 10557–10566. doi:10.1021/jp060292b

    Article  PubMed  CAS  Google Scholar 

  26. Estrada E (1996) Spectral moments of the edge adjacency matrix in molecular graphs. 1. Definition and applications for the prediction of physical properties of alkanes. J Chem Inf Comput Sci 36: 844–849. doi:10.1021/ci950187r

    CAS  Google Scholar 

  27. Estrada E (1997) Spectral moments of the edge adjacency matrix in molecular graphs. 2. Molecules containing heteroatoms and QSAR applications. J Chem Inf Comput Sci 37: 320–328. doi:10.1021/ci960113v

    CAS  Google Scholar 

  28. Estrada E (1998) Spectral moments of the edge adjacency matrix in molecular graphs. 3. Molecules containing cycles. J Chem Inf Comput Sci 38: 23–27. doi:10.1021/ci970030u

    CAS  Google Scholar 

  29. Estrada E (1998) Modeling the diamagnetic susceptibilities of organic compounds by a substructural graph theoretical approach. J Chem Soc, Faraday Trans 94: 1407–1411. doi:10.1039/a709032c

    Article  CAS  Google Scholar 

  30. Estrada E (1999) Generalized spectral moments of the interacted line graph sequence. A novel approach to QSPR Studies. J Chem Inf Comput Sci 39: 90–95. doi:10.1021/ci9800460

    CAS  Google Scholar 

  31. Estrada E, Peña A, García-Domenech R (1998) Designing sedative/hypnotic compounds from a novel structural graph theoretical-approach. J Comput Aided Mol Des 12: 583–595. doi:10.1023/A:1008048003720

    Article  PubMed  CAS  Google Scholar 

  32. Estrada E, Gutiérrez Y (1999) Modeling chromatographic parameters by a novel graph theoretical sub-structural approach. J Chromatogr A 858: 187–199. doi:10.1016/S0021-9673(99)00808-0

    Article  PubMed  CAS  Google Scholar 

  33. Estrada E, Gutiérrez Y, González H (2000) Modeling diamagnetic and magnetooptic properties of organic compounds with the TOSS-MODE approach. J Chem Inf Comput Sci 40: 1386–1399. doi:10.1021/ci000041e

    PubMed  CAS  Google Scholar 

  34. Estrada E, Peña A (2000) In silico studies for the rational discovery of anticonvulsant Compounds. Bioorg Med Chem 8: 2755–2770. doi:10.1016/S0968-0896(00)00204-2

    Article  PubMed  CAS  Google Scholar 

  35. Estrada E, Uriarte E, Montero A, Tejeira M, Santana L, De Clercq E (2000) A novel approach to the rational selection and design of anticancer compounds. J Med Chem 43: 1975–1985. doi:10.1021/jm991172d

    Article  PubMed  CAS  Google Scholar 

  36. Pérez-González M, González-Díaz H, Molina R, Cabrera MA, Ramos RA (2003) TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new herbicides. J Chem Inf Comput Sci 43: 1192–1199. doi:10.1021/ci034039+

    Google Scholar 

  37. Estrada E (1995) Edge adjacency relationship and a novel topological index related to molecular volume. J Chem Inf Comput Sci 35: 31–33. doi:10.1021/ci00023a004

    CAS  Google Scholar 

  38. Estrada E, Uriarte E (2001) Quantitative structure–toxicity relationship using TOPS-MODE. 1. Nitrobenzene toxicity to Tetrahymena pyriformis. SAR QSAR Environ Res 12: 309–324. doi:10.1080/10629360108032919

    Article  PubMed  CAS  Google Scholar 

  39. Estrada E, Molina E, Uriarte E (2001) Quantitative structure-toxicity relationship using TOPS-MODE. 2. Neurotoxicity of a noncongeneric series of solvents. SAR QSAR Environ Res 12: 445–459. doi:10.1080/10629360108035384

    Article  PubMed  CAS  Google Scholar 

  40. Estrada E, Uriarte E (2001) Recent advances on the role of topological indices in drug discovery research. Curr Med Chem 8: 1573–1588

    PubMed  CAS  Google Scholar 

  41. Kier LB, Hall LH (1981) Derivation and significance of valence molecular connectivity. J Pharm Sci 70: 583–589. doi:10.1002/jps.2600700602

    Article  PubMed  CAS  Google Scholar 

  42. Galvez J (1998) On a topological interpretation of electronic and vibrational molecular energies. J Mol Struct THEOCHEM 429: 255–264. doi:10.1016/S0166-1280(97)00366-7

    Article  CAS  Google Scholar 

  43. Estrada E (1999) Connectivity polynomial and long-range contributions in the molecular connectivity model. Chem Phys Lett 312: 556–560. doi:10.1016/S0009-2614(99)01007-6

    Article  CAS  Google Scholar 

  44. Kier LB, Hall LH (2000) The e-state as the basis for molecular structure space definition and structure similarity. J Chem Inf Comput Sci 40: 784–791. doi:10.1021/ci990140w

    PubMed  Google Scholar 

  45. Petitjean M (1992) Applications of the radius-diameter diagram to the classification of topological and geometrical shapes of chemical compounds. J Chem Inf Comput Sci 32: 331–337. doi:10.1021/ci00008a012

    CAS  Google Scholar 

  46. Estrada E, Molina E, Perdomo-López I (2001) Can 3D structural parameters be predicted from 2D (topological) molecular descriptors?. J Chem Inf Comput Sci 41: 1015–1021. doi:10.1021/ci000170v

    PubMed  CAS  Google Scholar 

  47. Bath PA, Poirrete AR, Willett P (1995) The extent of the relationship between the graph-theoretical and the geometrical shape coefficients of chemical compounds. J Chem Inf Comput Sci 35: 714–716. doi:10.1021/ci00026a007

    CAS  Google Scholar 

  48. Montero-Torres A, Vega MC, Marrero-Ponce Y, Rolón M, Gómez-Barrio A, Escario JA et al (2005) A novel non-stochastic quadratic fingerprints-based approach for the ‘in silico’ discovery of new antitrypanosomal compounds. Bioorg Med Chem 13: 6264–6275. doi:10.1016/j.bmc.2005.06.049

    Article  PubMed  CAS  Google Scholar 

  49. Marrero-Ponce Y, Iyarreta-Veitía M, Montero-Torres A, Romero-Zaldívar C, Brandt CA, Ávila P et al (2005) Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps. J Chem Inf Model 45: 1082–1100. doi:10.1021/ci050085t

    Article  PubMed  CAS  Google Scholar 

  50. González-Díaz H, Prado-Prado FJ, Santana L, Uriarte E (2006) Unify QSAR approach to antimicrobials. Part 1: predicting antifungal activity against different species–Bioorg Med Chem 1459735980. doi:10.1016/j.bmc.2006.05.018

    Google Scholar 

  51. Prado-Prado FJ, González-Díaz H, Santana L, Uriarte E (2007) Unify QSAR approach to antimicrobials. Part 2: predicting activity against more than 90 different species in order to halt antibacterial resistance–Bioorg Med Chem 15897902. doi:10.1016/j.bmc.2006.10.039

    Google Scholar 

  52. Randić M (1975) Characterization of molecular branching. J Am Chem Soc 97: 6609–6615. doi:10.1021/ja00856a001

    Article  Google Scholar 

  53. Estrada E, Vilar S, Uriarte E, Gutierrez Y (2002) In silico studies toward the discovery of new anti-HIV nucleoside compounds with the use of TOPS-MODE and 2D/3D connectivity indices. 1. Pyrimidyl derivatives. J Chem Inf Comput Sci 42: 1194–1203. doi:10.1021/ci0255331

    PubMed  CAS  Google Scholar 

  54. Gute BD, Balasubramanian K, Geiss KT, Basak SC (2004) Prediction of halocarbon toxicity from structure: a hierarchical QSAR approach. Environ Toxicol Pharmacol 16: 121–129. doi:10.1016/j.etap.2003.10.005

    Article  CAS  Google Scholar 

  55. Randić M (2001) The connectivity index 25 years after. J Mol Graph Model 20:19–35. doi:10.1016/S1093-3263(01)00098-5

    Article  PubMed  Google Scholar 

  56. Consonni V, Todeschini R, Pavan M (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 42: 682–692. doi:10.1021/ci015504a

    PubMed  CAS  Google Scholar 

  57. Consonni V, Todeschini R, Pavan M, Gramatica P (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies. J Chem Inf Comput Sci 42: 693–705. doi:10.1021/ci0155053

    PubMed  CAS  Google Scholar 

  58. Pérez-González M, Terán C, Teijeira M, González-Moa MJ (2005) GETAWAY descriptors to predicting A 2 A adenosine receptors agonists. Eur J Med Chem 40: 1080–1086. doi:10.1016/j.ejmech.2005.04.014

    Article  Google Scholar 

  59. Hocquet A, Langgård M (1998) An evaluation of the MM+ force field. J Mol Model 4: 94–112. doi:10.1007/s008940050128

    Article  CAS  Google Scholar 

  60. Dewar MJS, Zoebisch EG, Healy EF, Stewart JJP (1985) The development and use of quantum-mechanical molecular-models. 76. AM1 – a new general-purpose quantum-mechanical molecular-model. J Am Chem Soc 107: 3902–3909. doi:10.1021/ja00299a024

    Article  CAS  Google Scholar 

  61. Abraham MH (1993) Scales of solute hydrogen-bonding: their construction and application to physicochemicaI and biochemicaI processes. Chem Soc Rev 22: 73–83. doi:10.1039/cs9932200073

    Article  CAS  Google Scholar 

  62. Platts JA, Butina D, Abraham MH, Hersey A (1999) Estimation of molecular linear free energy relation descriptors using a group contribution approach. J Chem Inf Comput Sci 39: 835–845. doi:10.1021/ci980339t

    CAS  Google Scholar 

  63. Bren U, Martínek V, Florian J (2006) Free energy simulations of uncatalyzed DNA replication fidelity: structure and stability of T.G and dTTP.G terminal DNA mismatches flanked by a single dangling nucleotide. J Phys Chem B 110: 10557–10566. doi:10.1021/jp060292b

    Article  PubMed  CAS  Google Scholar 

  64. Klauda JB, Brooks BR (2007) Sugar binding in lactose permease: anomeric state of a disaccharide influences binding structure. J Mol Biol 367: 1523–1534. doi:10.1016/j.jmb.2007.02.001

    Article  PubMed  CAS  Google Scholar 

  65. Zagrovic B, van Gunsteren WF (2007) Computational analysis of the mechanism and thermodynamics of inhibition of phosphodiesterase 5A by synthetic ligands. J Chem Theory Comput 3: 301–311. doi:10.1021/ct600322d

    Article  CAS  Google Scholar 

  66. Kowalski RB, Wold S (1982) Pattern recognition in chemistry. In: Krishnaiah PR, Kanal LN (eds) Handbook of statistic. North Holland, Amsterdam, pp 673–697

    Google Scholar 

  67. McFarland JW, Gans DJ (1995) Cluster significance analysis. In: Manhnhold R, Krogsgaard-Larsen P, Timmerman H, Van Waterbeemd H (eds) Methods and principles in medicinal chemistry, vol 2, Chemometric methods in molecular design. VCH, Weinhiem, pp 295–308

    Google Scholar 

  68. Johnson RA, Wichern DW (1998) Applied multivariate statistical analysis. Prentice-Hall, NJ

    Google Scholar 

  69. Fawcett T (2004) ROC Graphs: notes and practical considerations for researchers. Technical report, Palo Alto, HP Laboratories USA

  70. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29–36

    PubMed  CAS  Google Scholar 

  71. Li R, Sirawaraporn R, Chitnumsub P, Sirawaraporn W, Wooden J, Athappilly F et al (2000) Three-dimensional structure of M. tuberculosis dihydrofolate reductase reveals opportunities for the design of novel tuberculosis drugs. J Mol Biol 295: 307–323. doi:10.1006/jmbi.1999.3328

    Article  PubMed  CAS  Google Scholar 

  72. Podust LM, Poulos TL, Waterman MR (2001) Crystal structure of cytochrome P450 14α-sterol demethylase (CyP51) from Mycobacterium tuberculosis in complex with azole inhibitors. Proc Natl Acad Sci USA 98: 3068–3073. doi:10.1073/pnas.061562898

    Article  PubMed  CAS  Google Scholar 

  73. Goulding CW, Parseghian A, Sawaya MR, Cascio D, Apostol MI, Gennaro ML et al (2002) Crystal structure of a major secreted protein of Mycobacterium tuberculosis-MPT63 at 1.5-Å resolution. Protein Sci 11: 2887–2893. doi:10.1110/ps.0219002

    Article  PubMed  CAS  Google Scholar 

  74. Lu H, Tonge PJ (2008) Inhibitors of FabI, drug target in the bacterial fatty acid biosynthesis pathway. Acc Chem Res 41: 11–20. doi:10.1021/ar700156e

    Article  PubMed  CAS  Google Scholar 

  75. Matsuura K, Yoshioka S, Tosha T, Hori H, Ishimori K, Kitagawa T et al (2005) Structural diversities of active site in clinical azole-bound forms between sterol 14α-demethylase (CYP51s) from Human and Mycobacterium tuberculosis. J Biol Chem 280: 9088–9096. doi:10.1074/jbc.M413042200

    Article  PubMed  CAS  Google Scholar 

  76. Spigelman MK (2007) New tuberculosis therapeutics: a growing pipeline. JID 196: 28–34. doi:10.1086/518663

    Article  Google Scholar 

  77. Maccari R, Ottaná R, Vigorita MG (2005) In vitro advanced antimycobacterial screening of isoniazid-related hydrazones, hydrazides, and cyanoboranes: Part 14. Bioorg Med Chem Lett 15: 2509–2513. doi:10.1016/j.bmcl.2005.03.065

    Article  PubMed  CAS  Google Scholar 

  78. Bedia KK, Elçin O, Seda U, Fatma K, Nathaly S, Sevim R et al (2006) Synthesis and characterization of novel hydrazide-hydrazones and the study of their structure-antituberculosis activity. Eur J Med Chem 41: 1253–1261. doi:10.1016/j.ejmech.2006.06.009

    Article  PubMed  CAS  Google Scholar 

  79. Stover CK, Warrener P, VanDevanter DR, Sherman DR, Arain TM, Langhorne MH et al (2000) A small-molecule nitroimidazopyran drug candidate for the treatment of tuberculosis. Nature 45: 962–966. doi:10.1038/35016103

    Article  Google Scholar 

  80. Cabrera MA, González I, Fernández C, Navarro C, Bermejo M (2006) A topological substructural approach for the prediction of p-glycoprotein substrates. J Pharm Sci 95: 589–606. doi:10.1002/jps.20449

    Article  PubMed  CAS  Google Scholar 

  81. Estrada E (2002) Physicochemical interpretation of molecular connectivity indices. J Phys Chem 106: 9085–9091

    CAS  Google Scholar 

  82. Van Waterbeemd H (1995) Discriminant analysis for activity prediction. In: Manhnhold R, Krogsgaard-Larsen P, Timmerman H, Waterbeemd H (eds) Methods and principles in medicinal chemistry, vol 2, Chemometric methods in molecular design. VCH, Weinhiem, pp 295–308

    Google Scholar 

  83. De Luca L (2006) Naturally occurring and synthetic imidazoles: their chemistry and their biological activity. Curr Med Chem 13: 1–23. doi:10.2174/092986706775197980

    Article  PubMed  CAS  Google Scholar 

  84. Huesca M, Al-Qawasmeh R, Young AH, Lee Y (2005) Patent WO 2005/047266 A1, International Application Number PCT/IB2004/052433

  85. Huesca M, Al-Qawasmeh R, Young AH, Lee Y (2004) Patent WO 2004/016086 A2, International Application Number PCT/CA2003/001229

  86. Khan MS, Siddiqui SA, Siddiqui MSRA, Goswami U, Srinivasan KV, Khan MI (2008) Antibacterial activity of synthetisized 2,4,5-trisubstituted imidazole derivatives. Chem Biol Drug Des 72: 197–204. doi:10.1111/j.1747-0285.2008.00691.x

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Chemistry, Faculty of Natural Sciences, University of Oriente, 90500, Santiago de Cuba, Cuba

    Alejandro Speck Planche & América García López

  2. Institute of Chemistry, University of São Paulo, Postal Box 26777, São Paulo, 05513-970, Brazil

    Alejandro Speck Planche & Vicente de Paulo Emerenciano

  3. Center of Applied Sciences and Education, Federal University of Paraiba, Campus IV, Rio Tinto, 58297-000, Brazil

    Marcus Tulius Scotti

  4. Faculty of Chemistry, University of Camagüey, 74650, Camagüey, Cuba

    Enrique Molina Pérez

  5. Health and Food Technologies Development Group, University of Camagüey, 74650, Camagüey, Cuba

    Enrique Molina Pérez

  6. Department of Organic Chemistry, University of Santiago de Compostela, 15982, Santiago de Compostela, Spain

    Eugenio Uriarte

Authors
  1. Alejandro Speck Planche
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcus Tulius Scotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. América García López
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vicente de Paulo Emerenciano
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Enrique Molina Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eugenio Uriarte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alejandro Speck Planche.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Cite this article

Planche, A.S., Scotti, M.T., López, A.G. et al. Design of novel antituberculosis compounds using graph-theoretical and substructural approaches. Mol Divers 13, 445–458 (2009). https://doi.org/10.1007/s11030-009-9129-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-009-9129-9

Keywords

Search

Navigation