Bulletin of the Korean Chemical Society

Korean Chemical Society (대한화학회)
권호 표지
DOI QR코드

DOI QR Code

The Predictive QSAR Model for hERG Inhibitors Using Bayesian and Random Forest Classification Method

  • Kim, Jun-Hyoung (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology) ;
  • Chae, Chong-Hak (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology) ;
  • Kang, Shin-Myung (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology) ;
  • Lee, Joo-Yon (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology) ;
  • Lee, Gil-Nam (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology) ;
  • Hwang, Soon-Hee (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology) ;
  • Kang, Nam-Sook (Drug Discovery Platform Technology Team, Korea Research Institute of Chemical Technology)
  • Received : 2010.11.20
  • Accepted : 2011.02.12
  • Published : 2011.04.20
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we have developed a ligand-based in-silico prediction model to classify chemical structures into hERG blockers using Bayesian and random forest modeling methods. These models were built based on patch clamp experimental results. The findings presented in this work indicate that Laplacian-modified naive Bayesian classification with diverse selection is useful for predicting hERG inhibitors when a large data set is not obtained.

Keywords

References

  1. Vandenberg, J. I.; Walker, B. D.; Campbell, T. J. Trends Pharmacol. Sci. 2001, 22, 240. https://doi.org/10.1016/S0165-6147(00)01662-X
  2. Brown, A. M. Cell Calcium 2004, 35, 543. https://doi.org/10.1016/j.ceca.2004.01.008
  3. Aronov, A. M. Drug Discov. Today 2005, 10, 149. https://doi.org/10.1016/S1359-6446(04)03278-7
  4. Mitcheson, J. S.; Chen, J.; Lin, M.; Culberson, C.; Sanguinetti, M. C. PNAS. 2000, 97, 12329. https://doi.org/10.1073/pnas.210244497
  5. Pearlstein, R. A.; Vaz, R. J.; Kang, J.; Chen, X.-L.; Preobrazhenskaya, M.; Shchekotikhin, A. E.; Korolev, A. M.; Lysenkova, L. N.; Miroshnikova, O. V.; Hendrix, J.; Rampe, D. Bioorg. Med. Chem. Lett. 2003, 13, 1829. https://doi.org/10.1016/S0960-894X(03)00196-3
  6. Du, L.; Li, M.; You, Q.; Xia, L. Biochem. Biophys. Res. Commun. 2007, 355, 889. https://doi.org/10.1016/j.bbrc.2007.02.068
  7. Ekins, S.; Crumb, W. J.; Sarazan, R. D.; Wikel, J. H.; Wrighton, S.A. J. Pharmacol. Exp. Ther. 2002, 301, 427. https://doi.org/10.1124/jpet.301.2.427
  8. Cavalli, A.; Poluzzi, E.; De Ponti, F.; Recanatini, M. J. Med. Chem. 2002, 45, 3844. https://doi.org/10.1021/jm0208875
  9. Keserü, G. M. Bioorg. Med. Chem. Lett. 2003, 13, 2773. https://doi.org/10.1016/S0960-894X(03)00492-X
  10. Thai, K. M.; Ecker, G. F. Chem. Biol. Drug Des. 2008, 72, 279. https://doi.org/10.1111/j.1747-0285.2008.00705.x
  11. Sun, H. ChemMedChem 2006, 1, 315. https://doi.org/10.1002/cmdc.200500047
  12. Gepp, M. M.; Hutter, M. C. Bioorg. Med. Chem. 2006, 14, 5325. https://doi.org/10.1016/j.bmc.2006.03.043
  13. Song, M.; Clark, M. J. Chem. Inf. Model. 2006, 46, 392. https://doi.org/10.1021/ci050308f
  14. Jia, L.; Sun, H. Bioorg. Med. Chem. 2008, 16, 6252. https://doi.org/10.1016/j.bmc.2008.04.028
  15. Tobita, M.; Nishikawa, T.; Nagashima, R. Bioorg. Med. Chem. Lett. 2005, 15, 2886. https://doi.org/10.1016/j.bmcl.2005.03.080
  16. Leong, M. K. Chem. Res. Toxicol. 2007, 20, 217. https://doi.org/10.1021/tx060230c
  17. Jaynes, E. T. Probability Theory: The Logic of Science. Cambridge University Press: London, 2003.
  18. Breiman, L. Mach. Learn. 2001, 45, 5. https://doi.org/10.1023/A:1010933404324
  19. Thomson Reuters $Integrity^{SM}$. Barcelona: Prous Science, S.A.U., a Thomson Reuters business. 2001. Available from: http://integrity.prous.com.
  20. Accelrys Software Inc., Pipeline Pilot Release 7.5, San Diego: Accelrys Software Inc., 2007.
  21. Glick, M.; Jenkins, J. L.; Nettles, J. H.; Hitchings, H.; Davies, J. W. J. Chem. Inf. Model. 2006, 46, 193. https://doi.org/10.1021/ci050374h

Cited by

  1. The Discovery of Aurora Kinase Inhibitor by Multi-Docking-Based Virtual Screening vol.15, pp.11, 2014, https://doi.org/10.3390/ijms151120403
  2. Synthesis, anticancer activity and pharmacokinetic analysis of 1-[(substituted 2-alkoxyquinoxalin-3-yl)aminocarbonyl]-4-(hetero)arylpiperazine derivatives vol.20, pp.3, 2012, https://doi.org/10.1016/j.bmc.2011.12.026
  3. The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis vol.25, pp.11, 2011, https://doi.org/10.3390/molecules25112615