Computational Design of Multi-target Kinase Inhibitors

  • Sinoy Sugunan
  • Rajanikant G. K. Email author
Part of the Methods in Pharmacology and Toxicology book series (MIPT)


As key regulators of every aspect of cell function, protein kinases are frequently associated with various human diseases. Therefore, protein kinase inhibition has become the second most important group of drug targets, after G-protein-coupled receptors. Owing to the complex and polygenic nature of diseases, designing multi-kinase small molecule inhibitors as potential therapies is gaining major consideration. Effective in silico drug design strategies are desired to identify multi-target kinase inhibitors. In this chapter, we summarize the two such effective computational strategies reported by our group to identify multi-target kinase inhibitors.


Ensemble pharmacophore Molecular docking Molecular dynamics simulation Multi-target inhibitor Protein kinase Virtual screening 


  1. 1.
    Wu P, Nielsen TE, Clausen MH (2016) Small-molecule kinase inhibitors: an analysis of FDA-approved drugs. Drug Discov Today 21:5–10CrossRefGoogle Scholar
  2. 2.
    Zhang J, Yang PL, Gray NS (2009) Targeting cancer with small molecule kinase inhibitors. Nat Rev Cancer 9:28–39CrossRefGoogle Scholar
  3. 3.
    Wu P, Nielsen TE, Clausen MH (2015) FDA-approved small-molecule kinase inhibitors. Trends Pharmacol Sci 36:422–439CrossRefGoogle Scholar
  4. 4.
    Yaish P, Gazit A, Gilon C et al (1988) Blocking of EGF-dependent cell proliferation by EGF receptor kinase inhibitors. Science 242:933–935CrossRefGoogle Scholar
  5. 5.
    Divito SJ, Kupper TS (2014) Inhibiting Janus kinases to treat alopecia areata. Nat Med 20:989–990CrossRefGoogle Scholar
  6. 6.
    Hendriks RW, Yuvaraj S, Kil LP (2014) Targeting Bruton’s tyrosine kinase in B cell malignancies. Nat Rev Cancer 14:219–232CrossRefGoogle Scholar
  7. 7.
    Wu P, Hu Y (2012) Small molecules targeting phosphoinositide 3-kinases. MedChemComm 3:1337–1355CrossRefGoogle Scholar
  8. 8.
    Wu P, Hu YZ (2010) PI3K/Akt/mTOR pathway inhibitors in cancer: a perspective on clinical progress. Curr Med Chem 17:4326–4341CrossRefGoogle Scholar
  9. 9.
    Li YH, Wang PP, Li XX et al (2016) The human kinome targeted by FDA approved multi-target drugs and combination products: a comparative study from the drug-target interaction network perspective. PLoS One 11:e0165737CrossRefGoogle Scholar
  10. 10.
    Rask-Andersen M, Zhang J, Fabbro D et al (2014) Advances in kinase targeting: current clinical use and clinical trials. Trends Pharmacol Sci 35:604–620CrossRefGoogle Scholar
  11. 11.
    Yang H, Qin C, Li YH et al (2016) Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res 44:D1069–D1074CrossRefGoogle Scholar
  12. 12.
    Boran ADW, Iyengar R (2010) Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Devel 13:297–309PubMedPubMedCentralGoogle Scholar
  13. 13.
    Dar AC, Das TK, Shokat KM et al (2012) Chemical genetic discovery of targets and anti-targets for cancer polypharmacology. Nature 486:80–84CrossRefGoogle Scholar
  14. 14.
    Korcsmáros T, Szalay MS, Böde C et al (2007) How to design multi-target drugs: target search options in cellular networks. Expert Opin Drug Discov 2:799–808CrossRefGoogle Scholar
  15. 15.
    Zhang S (2011) Computer-aided drug discovery and development. In: Satyanarayanajois SD (ed) Drug design and discovery: methods and protocols. Humana Press, Totowa, NJ, pp 23–38CrossRefGoogle Scholar
  16. 16.
    Ma XH, Shi Z, Tan C et al (2010) In-silico approaches to multi-target drug discovery. Pharm Res 27:739–749CrossRefGoogle Scholar
  17. 17.
    Clemente JC, Govindasamy L, Madabushi A et al (2006) Structure of the aspartic protease plasmepsin 4 from the malarial parasite Plasmodium malariae bound to an allophenylnorstatine-based inhibitor. Acta Crystallogr Sect D 62:246–252CrossRefGoogle Scholar
  18. 18.
    Wei D, Jiang X, Zhou L et al (2008) Discovery of multitarget inhibitors by combining molecular docking with common pharmacophore matching. J Med Chem 51:7882–7888CrossRefGoogle Scholar
  19. 19.
    Nair SB, Fayaz SM, Rajanikant GK (2013) A novel multi-target drug screening strategy directed against key proteins of DAPk family. Comb Chem High Throughput Screen 16:449–457CrossRefGoogle Scholar
  20. 20.
    Fayaz SM, Rajanikant GK (2015) Ensembling and filtering: an effective and rapid in silico multitarget drug-design strategy to identify RIPK1 and RIPK3 inhibitors. J Mol Model 21:314CrossRefGoogle Scholar
  21. 21.
    Zou J, Xie HZ, Yang SY et al (2008) Towards more accurate pharmacophore modeling: multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2. J Mol Graph Model 27:430–438CrossRefGoogle Scholar
  22. 22.
    Ren J, Xie L, Li WW et al (2010) SMAP-WS: a parallel web service for structural proteome-wide ligand-binding site comparison. Nucleic Acids Res 38:441–444CrossRefGoogle Scholar
  23. 23.
    Fayaz SM, Rajanikant GK (2014) Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors. J Comput Aided Mol Des 28:779–794CrossRefGoogle Scholar
  24. 24.
    Cui Z, Chen S, Wang Y et al (2017) Design, synthesis and evaluation of azaacridine derivatives as dual-target EGFR and Src kinase inhibitors for antitumor treatment. Eur J Med Chem 136:372–381CrossRefGoogle Scholar
  25. 25.
    Cui Z, Li X, Li L et al (2016) Design, synthesis and evaluation of acridine derivatives as multi-target Src and MEK kinase inhibitors for anti-tumor treatment. Bioorg Med Chem 24:261–269CrossRefGoogle Scholar
  26. 26.
    Reker D, Seet M, Pillong M et al (2014) Deorphaning pyrrolopyrazines as potent multi-target antimalarial agents. Angew Chem Int Ed 53:7079–7084CrossRefGoogle Scholar
  27. 27.
    Rehan M (2015) A structural insight into the inhibitory mechanism of an orally active PI3K/mTOR dual inhibitor, PKI-179 using computational approaches. J Mol Graph Model 62:226–234CrossRefGoogle Scholar
  28. 28.
    Ajmani S, Viswanadhan VN (2013) A neural network-based QSAR approach for exploration of diverse multi-tyrosine kinase inhibitors and its comparison with a fragment-based approach. Curr Comput Aided Drug Des 9:482–490CrossRefGoogle Scholar
  29. 29.
    Marzaro G, Chilin A, Guiotto A et al (2011) Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. Eur J Med Chem 46:2185–2192CrossRefGoogle Scholar
  30. 30.
    Wassermann AM, Peltason L, Bajorath J (2010) Computational analysis of multi-target structure-activity relationships to derive preference orders for chemical modifications toward target selectivity. ChemMedChem 5:847–858CrossRefGoogle Scholar
  31. 31.
    Allen BK, Mehta S, Ember SWJ et al (2015) Large-scale computational screening identifies first in class multitarget inhibitor of EGFR kinase and BRD4. Sci Rep 5:16924CrossRefGoogle Scholar
  32. 32.
    Hsu K-C, Cheng W-C, Chen Y-F et al (2012) Core site-moiety maps reveal inhibitors and binding mechanisms of orthologous proteins by screening compound libraries. PLoS One 7:e32142CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2018

Authors and Affiliations

  1. 1.School of BiotechnologyNational Institute of Technology CalicutCalicutIndia

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