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FTree query construction for virtual screening: a statistical analysis

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Abstract

FTrees (FT) is a known chemoinformatic tool able to condense molecular descriptions into a graph object and to search for actives in large databases using graph similarity. The query graph is classically derived from a known active molecule, or a set of actives, for which a similar compound has to be found. Recently, FT similarity has been extended to fragment space, widening its capabilities. If a user were able to build a knowledge-based FT query from information other than a known active structure, the similarity search could be combined with other, normally separate, fields like de-novo design or pharmacophore searches. With this aim in mind, we performed a comprehensive analysis of several databases in terms of FT description and provide a basic statistical analysis of the FT spaces so far at hand. Vendors’ catalogue collections and MDDR as a source of potential or known “actives”, respectively, have been used. With the results reported herein, a set of ranges, mean values and standard deviations for several query parameters are presented in order to set a reference guide for the users. Applications on how to use this information in FT query building are also provided, using a newly built 3D-pharmacophore from 57 5HT-1F agonists and a published one which was used for virtual screening for tRNA-guanine transglycosylase (TGT) inhibitors.

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References

  1. Willett P (2006) Drug Discov Today 11:1046

    Article  CAS  Google Scholar 

  2. Stiefl N, Zaliani A (2006) J Chem Inf Model 46:87

    Google Scholar 

  3. Stiefl N, Watson IA, Baumann K, Zaliani A (2006) J Chem Inf Model 46:208

    Article  CAS  Google Scholar 

  4. Fechner U, Paetz J, Schneider G (2005) QSAR Comb Sci 24(8):961

    Article  CAS  Google Scholar 

  5. Gillet VJ, Willett P, Bradshaw J (2003) J Chem Inf Comput Sci 43:338

    Article  CAS  Google Scholar 

  6. Bajorath J (2002) Nat Rev Drug Discov 1:882

    Article  CAS  Google Scholar 

  7. Bajorath J (2002) Drug Discov Today 7:1035

    Article  Google Scholar 

  8. Tanimoto TT (1957) IBM Internal Report

  9. Rarey M, Stahl M (2001) J Comput Aided Mol Des 15(6):497–520

    Article  CAS  Google Scholar 

  10. Rarey M, Dixon JS (1998) J Comput Aided Mol Des 12:471

    Article  CAS  Google Scholar 

  11. Bender A, Mussa HY, Gill GS, Glen RC (2004) J Med Chem 47:6569

    Article  CAS  Google Scholar 

  12. Evers A, Hessler G, Matter H, Klabunde T (2005) J Med Chem 48:5448

    Article  CAS  Google Scholar 

  13. Bissantz C, Schalon C, Guba W, Stahl M (2005) Proteins 61:938

    Article  CAS  Google Scholar 

  14. Schneider G, Schneider P, Renner S (2006) QSAR Comb Sci 25:1162

    Article  CAS  Google Scholar 

  15. Renner S, Schneider G (2006) ChemMedChem 1:181

    Article  CAS  Google Scholar 

  16. Klebe G (2006) Drug Discov Today 11:580

    Article  CAS  Google Scholar 

  17. Hartshorn MJ, Murray CA, Cleasby A, Frederickson M, Tickle IJ, Jhoti H (2005) J Med Chem 48:403

    Article  CAS  Google Scholar 

  18. Hajduk PJ, Huth JR, Fesik SW (2005) J Med Chem 48:2518

    Article  CAS  Google Scholar 

  19. Hessler G, Zimmermann M, Matter H, Evers A, Naumann T, Lengauer T, Rarey M (2005) J Med Chem 48:6575

    Article  CAS  Google Scholar 

  20. The MDL Drug Data Report is available from MDL Information Systems Inc., San Leandro CA

  21. Irwin JJ, Shoichet BK (2005) J Chem Inf Model 45:177

    Article  CAS  Google Scholar 

  22. http://blaster.docking.org/zinc/subset1 (accessed Dec 2007)

  23. Gasteiger J, Rudolph C, Sadowski J (1992) Tetrahedron Comput Method 3:537

    Article  Google Scholar 

  24. http://www.chemcomp.com (accessed Dec 2007)

  25. FTrees 1.5.1, http://www.biosolveit.de/FTrees (accessed Dec 2007) available from BioSolveIT, St. Augustin, Germany

  26. http://www.python.org (accessed Dec 2007)

  27. http://www.scipy.org (accessed Dec 2007)

  28. Filla SA, Mathes BM, Johnson KW, Phebus LA, Cohen ML, Nelson DL, Zgombick JM, Erickson JA, Schenck KW, Wainscott DB, Branchek TA, Schaus JM (2003) J Med Chem 46:3060

    Article  CAS  Google Scholar 

  29. Dixon SL, Smondyrev AM, Rao SN (2006) Chem Biol Drug Des 67:370

    Article  CAS  Google Scholar 

  30. Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) J Comput Aided Mol Des 20:647

    Article  CAS  Google Scholar 

  31. http://www.schroedinger.com (accessed Dec 2007)

  32. http://www.biosolveit.de/FTrees/download/ftrees_ug.pdf (accessed Dec 2007)

  33. Brenk R, Naerum L, Gradler U, Gerber HD, Garcia GA, Reuter K, Stubbs MT, Klebe G (2003) J Med Chem 46:1133

    Article  CAS  Google Scholar 

  34. Vieth M, Siegel MG, Higgs RE, Watson IA, Robertson DH, Savin KA, Durst GL, Hipskind PA (2004) J Med Chem 47:224

    Article  CAS  Google Scholar 

  35. Teague SJ, Davis AM, Leeson PD, Oprea T (1999) Angew Chem Int Ed 38:3743

    Article  CAS  Google Scholar 

  36. Laurie AT, Jackson RM (2005) Bioinformatics 21:1908

    Article  CAS  Google Scholar 

  37. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Adv Drug Deliv Rev 46:3

    Article  CAS  Google Scholar 

  38. Carr RA, Congreve M, Murray CW, Rees DC (2005) Drug Discov Today 10:987

    Article  CAS  Google Scholar 

  39. Rarey M, Stahl M (2001) J Comput Aided Mol Des 15:497

    Article  CAS  Google Scholar 

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Acknowledgements

We are in debt to Joerg Degen and Prof. Matthias Rarey (ZBH, Hamburg) for their suggestions and to the reviewers for their constructing criticism.

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Author notes

  1. Christof Gerlach

    Present address: Bayer-Schering Pharma AG, Müllerstrasse 178, 13342, Berlin, Germany

Authors and Affiliations

  1. Eli Lilly & Co. Research Laboratories, Essener Bogen 7, 22419, Hamburg, Germany

    Christof Gerlach & Andrea Zaliani

  2. Discovery Chemistry Research and Technologies, Lilly Research Laboratories, Centro de Investigacion Lilly, Avenida de la Industria 30, 28108, Alcobendas, Madrid, Spain

    Howard Broughton

  3. Zentrum für Bioinformatik, Universität Hamburg, Bundesstrasse 43, 20146, Hamburg, Germany

    Andrea Zaliani

Authors
  1. Christof Gerlach
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  2. Howard Broughton
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  3. Andrea Zaliani
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Correspondence to Andrea Zaliani.

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Gerlach, C., Broughton, H. & Zaliani, A. FTree query construction for virtual screening: a statistical analysis. J Comput Aided Mol Des 22, 111–118 (2008). https://doi.org/10.1007/s10822-008-9178-7

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  • DOI: https://doi.org/10.1007/s10822-008-9178-7

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