Skip to main content
SpringerLink
Log in

Consensus model for identification of novel PI3K inhibitors in large chemical library

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

Phosphoinositide 3-kinases (PI3Ks) inhibitors have treatment potential for cancer, diabetes, cardiovascular disease, chronic inflammation and asthma. A consensus model consisting of three base classifiers (AODE, kNN, and SVM) trained with 1,283 positive compounds (PI3K inhibitors), 16 negative compounds (PI3K non-inhibitors) and 64,078 generated putative negatives was developed for predicting compounds with PI3K inhibitory activity of IC50 ≤ 10 μM. The consensus model has an estimated false positive rate of 0.75%. Nine novel potential inhibitors were identified using the consensus model and several of these contain structural features that are consistent with those found to be important for PI3K inhibitory activities. An advantage of the current model is that it does not require knowledge of 3D structural information of the various PI3K isoforms, which is not readily available for all isoforms.

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
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Cantley LC (2002) Science 296:1655–1657

    Article  CAS  Google Scholar 

  2. Wymann MP, Zvelebil MJ, Laffargue M (2003) Trends Pharmacol Sci 24:366–376

    Article  CAS  Google Scholar 

  3. Marone R, Cmiljanovic V, Giese B, Wymann MP (2008) Biochim Biophys Acta, Proteins Proteomics 1784:159–185

    Article  CAS  Google Scholar 

  4. Knight ZA, Gonzalez B, Feldman ME, Zunder ER, Goldenberg DD, Williams O, Loewith R, Stokoe D, Balla A, Toth B, Balla T, Weiss WA, Williams RL, Shokat KM (2006) Cell 125:733–747

    Article  CAS  Google Scholar 

  5. Xie P, Williams DS, Atilla-Gokcumen GE, Milk L, Xiao M, Smalley KS, Herlyn M, Meggers E, Marmorstein R (2008) ACS Chem Biol 3:305–316

    Article  CAS  Google Scholar 

  6. Hayakawa M, Kaizawa H, Moritomo H, Koizumi T, Ohishi T, Okada M, Ohta M, Tsukamoto S, Parker P, Workman P, Waterfield M (2006) Bioorg Med Chem 14:6847–6858

    Article  CAS  Google Scholar 

  7. Kendall JD, Rewcastle GW, Frederick R, Mawson C, Denny WA, Marshall ES, Baguley BC, Chaussade C, Jackson SP, Shepherd PR (2007) Bioorg Med Chem 15:7677–7687

    Article  CAS  Google Scholar 

  8. Wee S, Lengauer C, Wiederschain D (2008) Curr Opin Oncol 20:77–82

    Article  CAS  Google Scholar 

  9. Pomel V, Klicic J, Covini D, Church DD, Shaw JP, Roulin K, Burgat-Charvillon F, Valognes D, Camps M, Chabert C, Gillieron C, Francon B, Perrin D, Leroy D, Gretener D, Nichols A, Vitte PA, Carboni S, Rommel C, Schwarz MK, Ruckle T (2006) J Med Chem 49:3857–3871

    Article  CAS  Google Scholar 

  10. Fischer PM (2008) Biotechnol J 3:452–470

    Article  CAS  Google Scholar 

  11. Seifert MH, Lang M (2008) Mini-Rev Med Chem 8:63–72

    Article  CAS  Google Scholar 

  12. Chen X, Wilson LJ, Malaviya R, Argentieri RL, Yang SM (2008) J Med Chem 51:7015–7019

    Article  CAS  Google Scholar 

  13. Truchon JF, Bayly CI (2007) J Chem Inf Model 47:488–508

    Article  CAS  Google Scholar 

  14. Frédérick R, Denny WA (2008) J Chem Inf Model 48:629–638

    Article  Google Scholar 

  15. RCSB Protein Data Bank. www.pdb.org. Accessed 6 Aug 2009

  16. Frédérick R, Mawson C, Kendall JD, Chaussade C, Rewcastle GW, Shepherd PR, Denny WA (2009) Bioorg Med Chem Lett 19:5842–5847

    Article  Google Scholar 

  17. Gramatica P (2007) QSAR Comb Sci 26:694–701

    Article  CAS  Google Scholar 

  18. Parker CN, Bajorath J (2006) QSAR Comb Sci 25:1153–1161

    Article  CAS  Google Scholar 

  19. Han LY, Ma XH, Lin HH, Jia J, Zhu F, Xue Y, Li ZR, Cao ZW, Ji ZL, Chen YZ (2008) J Mol Graphics Model 26:1276–1286

    Article  CAS  Google Scholar 

  20. Yap CW, Chen YZ (2005) J Chem Inf Model 45:982–992

    Article  CAS  Google Scholar 

  21. Lau QP, Wynne H, Mong Li L, Ying M, Liang C (2007) 19th IEEE International Conference on tools with artificial intelligence. ICTAI 1:350–357

    Google Scholar 

  22. Xue Y, Yap CW, Sun LZ, Cao ZW, Wang JF, Chen YZ (2004) J Chem Inf Comput Sci 44:1497–1505

    CAS  Google Scholar 

  23. Ma XH, Wang R, Yang SY, Li ZR, Xue Y, Wei YC, Low BC, Chen YZ (2008) J Chem Inf Model 48:1227–1237

    Article  CAS  Google Scholar 

  24. Schierz A (2009) Journal of Cheminformatics 1:21

    Article  Google Scholar 

  25. McGovern SL, Caselli E, Grigorieff N, Shoichet BK (2002) J Med Chem 45:1712–1722

    Article  CAS  Google Scholar 

  26. Shoichet BK (2004) Nature 432:862–865

    Article  CAS  Google Scholar 

  27. Liew CY, Ma XH, Liu X, Yap CW (2009) J Chem Inf Model 49:877–885

    Article  CAS  Google Scholar 

  28. Xue Y, Li ZR, Yap CW, Sun LZ, Chen X, Chen YZ (2004) J Chem Inf Comput Sci 44:1630–1638

    CAS  Google Scholar 

  29. CambridgeSoft Desktop Software–ChemDraw (Windows/Mac). http://www.cambridgesoft.com/. Accessed 6 Aug 2009

  30. CORINA: Generation of 3D coordinates. http://www.molecular-networks.com/software/corina/index.html. Accessed 6 Aug 2009

  31. MODEL reference manual. http://jing.cz3.nus.edu.sg/model/. Accessed Aug 6, 2009

  32. Perez JJ (2005) Chem Soc Rev 34:143–152

    Article  CAS  Google Scholar 

  33. Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T (2006) KDD ‘06: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining 935–940

  34. Tan P-N, Steinbach M, Kumar V (2005) Introduction to data mining. Addison Wesley

  35. Webb GI, Boughton JR, Wang Z (2005) MLear 58:5–24

    Google Scholar 

  36. Vapnik V (1995) The nature of statistical learning theory. Springer, New York, London

    Google Scholar 

  37. Czermiński R, Yasri A, Hartsough D (2001) Quant Struct-Act Relat 20:227–240

    Article  Google Scholar 

  38. Trotter M, Buxton B, Holden SB (2001) Measurement and Control 34:235–239

    Google Scholar 

  39. Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) ATLA Altern Lab Anim 33:445–459

    CAS  Google Scholar 

  40. Tropsha A, Gramatica P, Gombar Vijay K (2003) QSAR Comb Sci 22:69–77

    Article  CAS  Google Scholar 

  41. Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H (2000) Bioinformatics 16:412–424

    Article  CAS  Google Scholar 

  42. Matthews BW (1975) Biochim Biophys Acta 405:442–451

    CAS  Google Scholar 

  43. Yap CW, Xue Y, Li H, Li ZR, Ung CY, Han LY, Zheng CJ, Cao ZW, Chen YZ (2006) Mini-Rev Med Chem 6:449–459

    Article  CAS  Google Scholar 

Download references

Acknowledgments

Our appreciation to Professor Chen Yu Zong (Bioinformatics and Drug Design Group, National University of Singapore) for his valuable discussions.

Author information

Authors and Affiliations

  1. Pharmaceutical Data Exploration Laboratory, Department of Pharmacy, National University of Singapore, Singapore, Singapore

    Chin Yee Liew & Chun Wei Yap

  2. Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore

    Xiao Hua Ma

Authors
  1. Chin Yee Liew
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao Hua Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chun Wei Yap
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chun Wei Yap.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 117 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liew, C.Y., Ma, X.H. & Yap, C.W. Consensus model for identification of novel PI3K inhibitors in large chemical library. J Comput Aided Mol Des 24, 131–141 (2010). https://doi.org/10.1007/s10822-010-9321-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-010-9321-0

Keywords

Search

Navigation