Skip to main content
SpringerLink
Log in

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds

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

Abstract

The discovery of novel scaffolds against a specific target has long been one of the most significant but challengeable goals in discovering lead compounds. A scaffold that binds in important regions of the active pocket is more favorable as a starting point because scaffolds generally possess greater optimization possibilities. However, due to the lack of sufficient chemical space diversity of the databases and the ineffectiveness of the screening methods, it still remains a great challenge to discover novel active scaffolds. Since the strengths and weaknesses of both fragment-based drug design and traditional virtual screening (VS), we proposed a fragment VS concept based on Bayesian categorization for the discovery of novel scaffolds. This work investigated the proposal through an application on VEGFR-2 target. Firstly, scaffold and structural diversity of chemical space for 10 compound databases were explicitly evaluated. Simultaneously, a robust Bayesian classification model was constructed for screening not only compound databases but also their corresponding fragment databases. Although analysis of the scaffold diversity demonstrated a very unevenly distribution of scaffolds over molecules, results showed that our Bayesian model behaved better in screening fragments than molecules. Through a literature retrospective research, several generated fragments with relatively high Bayesian scores indeed exhibit VEGFR-2 biological activity, which strongly proved the effectiveness of fragment VS based on Bayesian categorization models. This investigation of Bayesian-based fragment VS can further emphasize the necessity for enrichment of compound databases employed in lead discovery by amplifying the diversity of databases with novel structures.

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

Similar content being viewed by others

References

  1. Wassermann AM, Kutchukian PS, Lounkine E, Luethi T, Hamon J, Bocker MT, Malik HA, Cowan-Jacob SW, Glick M (2013) Efficient search of chemical space: navigating from fragments to structurally diverse chemotypes. J Med Chem 56:8879–8891. doi:10.1021/jm401309q

    Article  CAS  PubMed  Google Scholar 

  2. Rees DC, Congreve M, Murray CW, Carr R (2004) Fragment-based lead discovery. Nat Rev Drug Discov 3:660–672. doi:10.1038/nrd1467

    Article  CAS  PubMed  Google Scholar 

  3. Congreve M, Chessari G, Tisi D, Woodhead AJ (2008) Recent developments in fragment-based drug discovery. J Med Chem 51:3661–3680. doi:10.1021/jm8000373

    Article  CAS  PubMed  Google Scholar 

  4. Walters WP, Stahl MT, Murcko MA (1998) Virtual screening-an overview. Drug Discov Today 3:160–178. doi:10.1016/S1359-6446(97)01163-X

    Article  CAS  Google Scholar 

  5. Kumar A, Voet A, Zhang K (2012) Fragment based drug design: from experimental to computational approaches. Curr Med Chem 19:5128–5147. doi:10.2174/092986712803530467

    Article  CAS  PubMed  Google Scholar 

  6. Pierce AC, Rao G, Bemis GW (2004) BREED: generating novel inhibitors through hybridization of known ligands. Application to CDK2, p38, and HIV protease. J Med Chem 47:2768–2775. doi:10.1021/jm030543u

    Article  CAS  PubMed  Google Scholar 

  7. Bemis GW, Murcko MA (1996) The properties of known drugs. 1. Molecular frameworks. J Med Chem 39:2887–2893. doi:10.1021/jm9602928

    Article  CAS  PubMed  Google Scholar 

  8. Lewell XQ, Judd DB, Watson SP, Hann MM (1998) RECAP retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci 38:511–522. doi:10.1021/ci970429i

    Article  CAS  PubMed  Google Scholar 

  9. Schuffenhauer A, Ertl P, Roggo S, Wetzel S, Koch MA, Waldmann H (2007) The scaffold tree-visualization of the scaffold universe by hierarchical scaffold classification. J Chem Inf Model 47:47–58. doi:10.1021/ci600338x

    Article  CAS  PubMed  Google Scholar 

  10. Baker M (2012) Fragment-based lead discovery grows up. Nat Rev Drug Discov 12:5–7. doi:10.1038/nrd3926

    Article  Google Scholar 

  11. Law R, Barker O, Barker JJ, Hesterkamp T, Godemann R, Andersen O, Fryatt T, Courtney S, Hallett D, Whittaker M (2009) The multiple roles of computational chemistry in fragment-based drug design. J Comput Aided Mol Des 23:459–473. doi:10.1007/s10822-009-9284-1

    Article  CAS  PubMed  Google Scholar 

  12. Yuan H, Tai W, Hu S, Liu H, Zhang Y, Yao S, Ran T, Lu S, Ke Z, Xiong X (2013) Fragment-based strategy for structural optimization in combination with 3D-QSAR. J Comput Aided Mol Des 27:897–915. doi:10.1007/s10822-013-9687-x

    Article  CAS  PubMed  Google Scholar 

  13. Hou T, Xu X (2004) Recent development and application of virtual screening in drug discovery: an overview. Curr Pharm Des 10:1011–1033. doi:10.2174/1381612043452721

    Article  CAS  PubMed  Google Scholar 

  14. Xia X, Maliski EG, Gallant P, Rogers D (2004) Classification of kinase inhibitors using a Bayesian model. J Med Chem 47:4463–4470. doi:10.1021/jm0303195

    Article  CAS  PubMed  Google Scholar 

  15. Xie Q-Q, Zhong L, Pan Y-L, Wang X-Y, Zhou J-P, Di-wu L, Huang Q, Wang Y-L, Yang L-L, Xie H-Z (2011) Combined SVM-based and docking-based virtual screening for retrieving novel inhibitors of c-Met. Eur J Med Chem 46:3675–3680. doi:10.1016/j.ejmech.2011.05.031

    Article  CAS  PubMed  Google Scholar 

  16. Singh N, Chaudhury S, Liu R, AbdulHameed MDM, Tawa G, Wallqvist A (2012) QSAR classification model for antibacterial compounds and its use in virtual screening. J Chem Inf Model 52:2559–2569. doi:10.1021/ci300336v

    Article  CAS  PubMed  Google Scholar 

  17. Vijayan R, Bera I, Prabu M, Saha S, Ghoshal N (2009) Combinatorial library enumeration and lead hopping using comparative interaction fingerprint analysis and classical 2D QSAR methods for seeking novel GABAA \({\alpha } 3\) modulators. J Chem Inf Model 49:2498–2511. doi:10.1021/ci900309s

    Article  CAS  PubMed  Google Scholar 

  18. Lee JH, Lee S, Choi S (2010) In silico classification of adenosine receptor antagonists using Laplacian-modified naive Bayesian, support vector machine, and recursive partitioning. J Mol Graph Model 28:883–890. doi:10.1016/j.jmgm.2010.03.008

    Article  CAS  PubMed  Google Scholar 

  19. Prathipati P, Ma NL, Keller TH (2008) Global Bayesian models for the prioritization of antitubercular agents. J Chem Inf Model 48:2362–2370. doi:10.1021/ci800143n

    Article  CAS  PubMed  Google Scholar 

  20. Wang S, Li Y, Wang J, Chen L, Zhang L, Yu H, Hou T (2012) ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage. Mol Pharm 9:996–1010. doi:10.1021/mp300023x

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Hu Y, Unwalla R, Denny RA, Bikker J, Di L, Humblet C (2010) Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability. J Comput Aided Mol Des 24:23–35. doi:10.1007/s10822-009-9309-9

    Article  CAS  PubMed  Google Scholar 

  22. Kombo DC, Bencherif M (2013) Comparative study on the use of Docking and Bayesian categorization to predict ligand binding to nicotinic acetylcholine receptors (nAChRs) subtypes. J Chem Inf Model 53:3212–3222. doi:10.1021/ci400493a

    Article  CAS  PubMed  Google Scholar 

  23. Yuan H, Lu T, Ran T, Liu H, Lu S, Tai W, Leng Y, Zhang W, Wang J, Chen Y (2011) Novel strategy for three-dimensional fragment-based lead discovery. J Chem Inf Model 51:959–974. doi:10.1021/ci200003c

  24. Kiselyov A, Balakin KV, Tkachenko SE (2007) VEGF/VEGFR signalling as a target for inhibiting angiogenesis. Expert Opin Investig Drugs 16:83–107. doi:10.1517/13543784.16.1.83

    Article  CAS  PubMed  Google Scholar 

  25. Huang L, Huang Z, Bai Z, Xie R, Sun L, Lin K (2012) Development and strategies of VEGFR-2/KDR inhibitors. Future Med Chem 4:1839–1852. doi:10.4155/fmc.12.121

    Article  CAS  PubMed  Google Scholar 

  26. Boyer SJ (2002) Small molecule inhibitors of KDR (VEGFR-2) kinase: an overview of structure activity relationships. Curr Top Med Chem 2:973–1000. doi:10.2174/1568026023393273

    Article  CAS  PubMed  Google Scholar 

  27. Musumeci F, Radi M, Brullo C, Schenone S (2012) Vascular endothelial growth factor (VEGF) receptors: drugs and new inhibitors. J Med Chem 55:10797–10822. doi:10.1021/jm301085w

    Article  CAS  PubMed  Google Scholar 

  28. Zhang Y, Liu H, Jiao Y, Yuan H, Wang F, Lu S, Yao S, Ke Z, Tai W, Jiang Y (2012) De novo design of N-(pyridin-4-ylmethyl) aniline derivatives as KDR inhibitors: 3D-QSAR, molecular fragment replacement, protein-ligand interaction fingerprint, and ADMET prediction. Mol Divers 16:787–802. doi:10.1007/s11030-012-9405-y

    Article  PubMed  Google Scholar 

  29. Zhang Y, Yang S, Jiao Y, Liu H, Yuan H, Lu S, Ran T, Yao S, Ke Z, Xu J (2013) An Integrated Virtual Screening Approach for VEGFR-2 Inhibitors. J Chem Inf Model 53:3163–3177. doi:10.1021/ci400429g

    Article  CAS  PubMed  Google Scholar 

  30. Socinski MA (2011) Multitargeted receptor tyrosine kinase inhibition: an antiangiogenic strategy in non-small cell lung cancer. Cancer Treat Rev 37:611–617. doi:10.1016/j.ctrv.2011.04.003

    Article  CAS  PubMed  Google Scholar 

  31. Langdon SR, Brown N, Blagg J (2011) Scaffold diversity of exemplified medicinal chemistry space. J Chem Inf Model 51:2174–2185. doi:10.1021/ci2001428

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  32. Shen M, Tian S, Li Y, Li Q, Xu X, Wang J, Hou T (2012) Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines. J Cheminformatics 4:1–13. doi:10.1186/1758-2946-4-31

    Article  CAS  Google Scholar 

  33. Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582–6594. doi:10.1021/jm300687e

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  34. Inc. AS (2008) Pilot Pipeline version 7.5. Accelrys Software Inc: San Diego

  35. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36:D901–D906. doi:10.1093/nar/gkm958

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42:D1091–D1097. doi:10.1093/nar/gkt1068

    Article  CAS  PubMed  Google Scholar 

  37. McGregor MJ, Pallai PV (1997) Clustering of large databases of compounds: using the MDL “keys” as structural descriptors. J Chem Inf Comput Sci 37:443–448. doi:10.1021/ci960151e

    Article  CAS  Google Scholar 

  38. Butina D (1999) Unsupervised data base clustering based on daylight’s fingerprint and Tanimoto similarity: a fast and automated way to cluster small and large data sets. J Chem Inf Comput Sci 39:747–750. doi:10.1021/ci9803381

    Article  CAS  Google Scholar 

  39. Leeson P (2012) Drug discovery: chemical beauty contest. Nature 481:455–456. doi:10.1038/481455a

    Article  CAS  PubMed  Google Scholar 

  40. Ihlenfeldt W-D, Voigt JH, Bienfait B, Oellien F, Nicklaus MC (2002) Enhanced CACTVS browser of the open NCI database. J Chem Inf Comput Sci 42:46–57. doi:10.1021/ci010056s

    Article  CAS  PubMed  Google Scholar 

  41. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768. doi:10.1021/ci3001277

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  42. Zauhar RJ, Gianti E, Welsh WJ (2013) Fragment-based shape signatures: a new tool for virtual screening and drug discovery. J Comput Aided Mol Des 27:1009–1036. doi:10.1007/s10822-013-9698-7

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  43. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107. doi:10.1093/nar/gkr777

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  44. van Linden OP, Kooistra AJ, Leurs R, de Esch IJ, de Graaf C (2013) KLIFS: a knowledge-based structural database to navigate kinase-ligand interaction space. J Med Chem 57:249–277. doi:10.1021/jm400378w

    Article  PubMed  Google Scholar 

  45. Kirchmair J, Markt P, Distinto S, Schuster D, Spitzer GM, Liedl KR, Langer T, Wolber G (2008) The Protein Data Bank (PDB), its related services and software tools as key components for in silico guided drug discovery. J Med Chem 51:7021–7040. doi:10.1021/jm8005977

    Article  CAS  PubMed  Google Scholar 

  46. Mauser H, Stahl M (2007) Chemical fragment spaces for de novo design. J Chem Inf Model 47:318–324. doi:10.1021/ci6003652

    Article  CAS  PubMed  Google Scholar 

  47. Shneiderman B (1992) Tree visualization with tree-maps: 2-d space-filling approach. ACM Trans Graph 11:92–99. doi:10.1145/102377.115768

    Article  Google Scholar 

  48. Krier M, Bret G, Rognan D (2006) Assessing the scaffold diversity of screening libraries. J Chem Inf Model 46:512–524. doi:10.1021/ci050352v

    Article  CAS  PubMed  Google Scholar 

  49. Kibbey C, Calvet A (2005) Molecular Property eXplorer: a novel approach to visualizing SAR using tree-maps and heatmaps. J Chem Inf Model 45:523–532. doi:10.1021/ci0496954

    Article  CAS  PubMed  Google Scholar 

  50. Clark AM (2009) 2D depiction of fragment hierarchies. J Chem Inf Model 50:37–46. doi:10.1021/ci900350h

    Article  PubMed Central  Google Scholar 

  51. Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y, Humblet C (2009) Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model 49:1455–1474. doi:10.1021/ci900056c

    Article  CAS  PubMed  Google Scholar 

  52. Kontoyianni M, McClellan LM, Sokol GS (2004) Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 47:558–565. doi:10.1021/jm0302997

    Article  CAS  PubMed  Google Scholar 

  53. Fraley ME, Arrington KL, Buser CA, Ciecko PA, Coll KE, Fernandes C, Hartman GD, Hoffman WF, Lynch JJ, McFall RC (2004) Optimization of the indolyl quinolinone class of KDR (VEGFR-2) kinase inhibitors: effects of 5-amido-and 5-sulphonamido-indolyl groups on pharmacokinetics and hERG binding. Bioorg Med Chem Lett 14:351–355. doi:10.1016/j.bmcl.2003.11.007

    Article  CAS  PubMed  Google Scholar 

  54. Dinges J, Akritopoulou-Zanze I, Arnold LD, Barlozzari T, Bousquet PF, Cunha GA, Ericsson AM, Iwasaki N, Michaelides MR, Ogawa N (2006) Hit-to-lead optimization of 1, 4-dihydroindeno [1, 2-c] pyrazoles as a novel class of KDR kinase inhibitors. Bioorg Med Chem Lett 16:4371–4375. doi:10.1016/j.bmcl.2006.05.052

    Article  CAS  PubMed  Google Scholar 

  55. Akritopoulou-Zanze I, Albert DH, Bousquet PF, Cunha GA, Harris CM, Moskey M, Dinges J, Stewart KD, Sowin TJ (2007) Synthesis and biological evaluation of 5-substituted 1, 4-dihydroindeno [1, 2-c] pyrazoles as multitargeted receptor tyrosine kinase inhibitors. Bioorg Med Chem Lett 17:3136–3140. doi:10.1016/j.bmcl.2007.03.031

  56. Dinges J, Ashworth KL, Akritopolou-Zanze I, Arnold LD, Baumeister SA, Bousquet PF, Cunha GA, Davidsen SK, Djuric SW, Gracias VJ (2006) 1, 4-Dihydroindeno [1, 2-c] pyrazoles as novel multitargeted receptor tyrosine kinase inhibitors. Bioorg Med Chem Lett 16:4266–4271. doi:10.1016/j.bmcl.2006.05.066

    Article  CAS  PubMed  Google Scholar 

  57. Dinges J, Albert DH, Arnold LD, Ashworth KL, Akritopoulou-Zanze I, Bousquet PF, Bouska JJ, Cunha GA, Davidsen SK, Diaz GJ (2007) 1, 4-Dihydroindeno [1, 2-c] pyrazoles with acetylenic side chains as novel and potent multitargeted receptor tyrosine kinase inhibitors with low affinity for the hERG ion channel. J Med Chem 50:2011–2029. doi:10.1021/jm061223o

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was financially supported by National Natural Science Foundation of China (81302634, 21302225 and NSFC81473077), the Fundamental Research Funds for the Central Universities (PT2014LX0072); the Natural Science Foundation of Jiangsu Province (BK20130662), the Postgraduate Innovative Foundation supported by Jiangsu Province (KYLX_0637), and the Jiangsu Qinglan Project.

Conflict of interest

The authors declare no competing financial interest.

Author information

Authors and Affiliations

  1. Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China

    Yanmin Zhang, Xiao Xiong, Haichun Liu, Ting Ran, Jinxing Xu, Shuai Lu, Anyang Xu, Jing Pan, Xin Qiao, Tao Lu & Yadong Chen

  2. School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China

    Yu Jiao, Zhihao Shi & Tao Lu

Authors
  1. Yanmin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haichun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ting Ran
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinxing Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shuai Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Anyang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jing Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xin Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Zhihao Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Tao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yadong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Tao Lu or Yadong Chen.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 1699 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Jiao, Y., Xiong, X. et al. Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds. Mol Divers 19, 895–913 (2015). https://doi.org/10.1007/s11030-015-9592-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-015-9592-4

Keywords

Search

Navigation