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A radial-distribution-function approach for predicting rodent carcinogenicity

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Abstract

Carcinogenic activity has been investigated using the Radial-Distribution-Function (RDF) approach. A discriminant model was developed to predict the carcinogenic and non-carcinogenic activity on a data set of 188 compounds. The percentage of overall classification was 76.4% for the carcinogenic chemicals and 72.5% for the non-carcinogenic chemicals. The predictive power of the model was validated by two tests: a cross-validation by the resubstitution technique and a test set (compounds not used in the development of the model) with 79.3 and 72.5% good classification, respectively. The RDF descriptors were compared with eight other methodologies; Constitutional, Molecular walks counts, Galvez topological charge indices, 2D autocorrelations, Randić molecular profiles, Geometrical, 3D-MORSE, and WHIM, demonstrating that the RDF descriptors are better to the rest of the approaches used.

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References

  1. Kennedy T (1997) Drug Disc Today (DDT) 2:436–444

    Article  Google Scholar 

  2. Modi S (2003) Drug Discov Today (DDT) 8:621–623

    Article  Google Scholar 

  3. van de Waterbeemd H, Gifford E (2003) Nat Rev Drug Discov 2:192–204

    Article  Google Scholar 

  4. Dearden JC (2003) J Comput-Aided Mol Des 17:119–127

    Article  CAS  Google Scholar 

  5. Benigni R, Giuliani A (2003) Bioinformatics 19:1194–1200

    Article  CAS  Google Scholar 

  6. Haseman J, Melnick R, Tomatis L, Huff J (2001) J Food Chem Toxicol 39:739–744

    Article  CAS  Google Scholar 

  7. Fung VA, Barrett JC, Huff J (1995) Environ Health Perspect 103:680–683

    Article  CAS  Google Scholar 

  8. Huff J (1993) Environ Health Perspect 100:201–210

    Article  CAS  Google Scholar 

  9. Richard AM (1998) Toxicol Lett 102–103:611–616

    Article  Google Scholar 

  10. Richard AM, Beingni R (2002) SAR QSAR Envirom Res 13:1–19

    Article  CAS  Google Scholar 

  11. Morales AH, Cabrera MA, Combes RD, Gonzalez MP (2005) Current Comp Aid Drug Des 3:237–255

    Google Scholar 

  12. Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley-VCH, Weinheim, Germany

    Google Scholar 

  13. González MP, Terán C, Fall Y, Teijeira M, Besada P (2005) Bioorg Med Chem 13:601–608

    Article  Google Scholar 

  14. Yan A, Gasteiger J (2003) J Chem Inf Comput Sci 43:429–434

    Article  CAS  Google Scholar 

  15. Wegner JK, Frohlich H, Zell A (2004) J Chem Inf Comput Sci 44:931–939

    Article  CAS  Google Scholar 

  16. Gasteiger J, Schuur J, Selzer P, Steinhauer L, Steinhauer V, Fresenius J (1997) Anal Chem 359:50–59

    Article  CAS  Google Scholar 

  17. Hemmer MC, Steinhauer V, Gasteiger J (1999) J Vibra Spectros 19:151–164

    Article  CAS  Google Scholar 

  18. Gasteiger J, Sadowski J, Schuur J, Selzer P, Steinhauer L, Steinhauer V (1996) J Chem Inf Comput Sci 36:1030–1037

    CAS  Google Scholar 

  19. Gold LS, Manley NB, Slone TH, Rohrbach L (1999) Environ Health Perspect Suppl 107:527–532

    Article  CAS  Google Scholar 

  20. Enslein K (1999) HDI Comput Toxicol NEWS 22

  21. González MP, González HD, Molina RR, Cabrera MA, Ramos de Armas R (2003) J Chem Inf Comput Sci 43:1192–1199

    Article  Google Scholar 

  22. González MP, Días LC, Morales AH, Rodríguez YM, Gonzaga de Oliveira L, Gómez LT, González HD (2004) Bioorg Med Chem 12:4467–4475

    Article  Google Scholar 

  23. McFarland JW, Gans DJ (1995) Cluster Significance Analysis. In: Manhnhold R, Krogsgaard-Larsen P, Timmerman H (eds) Method and Principles in Medicinal Chemistry, vol 2. In: van Waterbeemd H (ed) Chemometric methods in molecular design. VCH, Weinheim, pp 295–307

  24. Johnson RA, Wichern DW (1988) Applied MultiVariate Statistical Analysis. Prentice-Hall, New York

    Google Scholar 

  25. Michael JS, Dewar E, Zoebisch G, Eamonn F, Stewart JP (1985) J Am Chem Soc 107:3902–3909

    Article  Google Scholar 

  26. Stewart JJP (1990) MOPAC manual, 6th edn. Frank J Seiler Research Laboratory, US Air Force academy, Colorado Springs, CO, p 189

  27. Todeschini R, Consonni V, Pavan M (2002) Dragon Software, version 2.1

  28. StatSoft Inc (2002) STATISTICA 6.0, version 6.0

  29. Randić M (1991) J Chem Inf Comput Sci 31:311–320

    Google Scholar 

  30. Randić M (1991) New J Chem 15:517–525

    Google Scholar 

  31. Randić M (1991) J Mol Struct (Theochem) 233:45–59

    Article  Google Scholar 

  32. Lučić B, Nikolić S, Trinajstić N (1995) J Chem Inf Comput Sci 35:532–538

    Article  Google Scholar 

  33. Kowalski RB, Wold S (1982) Pattern recognition in chemistry. In: Krishnaiah PR, Kanal LN (eds) Handbook of statistics. North Holland ""delete Publishing Company, Amsterdam 673–697

    Google Scholar 

  34. Hawkins DM (2004) J Chem Inf Comput Sci 44:1–12

    Article  CAS  Google Scholar 

  35. González-Díaz H, Ramos R, Molina RR (2003) Bioinformatics 19:2079–2087

    Article  Google Scholar 

  36. González-Díaz H, Marrero Y, Hernández I, Bastida I, Tenorio E, Nasco O, Uriarte E, Castañedo NC, Cabrera-Pérez MA, Aguila E, Marrero O, Morales A, González MP (2003) Chem Res Toxicol 16:1318–1327

    Article  Google Scholar 

  37. González-Díaz H, Bastida I, Castañedo N, Nasco O, Olazabal E, Morales A, Serrano HS, Ramos R (2004) Bull Math Biol 66:1285–1311

    Article  Google Scholar 

  38. González-Díaz H, Uriarte E, Ramos R (2005) Bioorg Med Chem 13:323–331

    Article  Google Scholar 

  39. Klein DJ, Randić M, Babic D, Lucic B, Nikolic S, Trinajstic N (1997) Int J Quantum Chem 63:215–221

    Article  CAS  Google Scholar 

  40. Contrera JF, Matthews EJ, Benz RD (2003) Regul Toxicol Pharmacol 38:243–259

    Article  CAS  Google Scholar 

  41. Franke R, Gruska A, Giuliani A, Benigni R (2001) Carcinogenesis 22:1561–1571

    Article  CAS  Google Scholar 

  42. Benigni R, Richard AM (1996) Mutat Res 371:29–46

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors would like to express gratitude to Dr. Romualdo Benigni and Dr. Johann Gasteiger for sending us useful information for the development of this work.

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Authors and Affiliations

  1. Department of Chemistry, Central University of Las Villas, Santa Clara, Villa Clara, 54830, Cuba

    Aliuska Morales Helguera

  2. Department of Drug Design, Chemical Bioactives Center, Central University of Las Villas, Santa Clara, Villa Clara, 54830, Cuba

    Aliuska Morales Helguera, Miguel Ángel Cabrera Pérez & Maykel Pérez González

  3. Unit of Services, Department of Drug Design, Experimental Sugar Cane Station “Villa Clara-Cienfuegos”, Ranchuelo, Villa Clara, 53100, Cuba

    Maykel Pérez González

Authors
  1. Aliuska Morales Helguera
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  2. Miguel Ángel Cabrera Pérez
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  3. Maykel Pérez González
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Correspondence to Maykel Pérez González.

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Helguera, A.M., Cabrera Pérez, M.Á. & González, M.P. A radial-distribution-function approach for predicting rodent carcinogenicity. J Mol Model 12, 769–780 (2006). https://doi.org/10.1007/s00894-005-0088-5

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  • DOI: https://doi.org/10.1007/s00894-005-0088-5

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