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Bioactivity-guided mapping and navigation of chemical space


The structure- and chemistry-based hierarchical organization of library scaffolds in tree-like arrangements provides a valid, intuitive means to map and navigate chemical space. We demonstrate that scaffold trees built using bioactivity as the key selection criterion for structural simplification during tree construction allow efficient and intuitive mapping, visualization and navigation of the chemical space defined by a given library, which in turn allows correlation of this chemical space with the investigated bioactivity and further compound design. Brachiation along the branches of such trees from structurally complex to simple scaffolds with retained yet varying bioactivity is feasible at high frequency for the five major pharmaceutically relevant target classes and allows for the identification of new inhibitor types for a given target. We provide proof of principle by identifying new active scaffolds for 5-lipoxygenase and the estrogen receptor ERα.

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Figure 1: Bioactivity-guided construction of a scaffold tree exemplified for paclitaxel-derived tubulin stabilizers and indole alkaloids active on the 5-hydroxytryptamine receptor 5HT-2A.
Figure 2: Selected examples of identified scaffold branches.
Figure 3: Brachiation within major target classes.
Figure 4: Distribution of brachiation length and size (number of rings in the largest scaffold) of the scaffolds.
Figure 5: Brachiation-based identification of new active scaffolds for 5-lipoxygenase and for ERα.

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  1. Koch, M.A. et al. Charting biologically relevant chemical space: a structural classification of natural products (SCONP). Proc. Natl. Acad. Sci. USA 102, 17272–17277 (2005).

    Article  CAS  PubMed  Google Scholar 

  2. Schuffenhauer, A. et al. The scaffold tree - visualization of the scaffold universe by hierarchical scaffold classification. J. Chem. Inf. Model. 47, 47–58 (2007).

    Article  CAS  PubMed  Google Scholar 

  3. Clark, A.M. & Labute, P. Detection and assignment of common scaffolds in project databases of lead molecules. J. Med. Chem. 52, 469–483 (2009).

    Article  CAS  PubMed  Google Scholar 

  4. Kaiser, M., Wetzel, S., Kumar, K. & Waldmann, H. Biology-inspired synthesis of compound libraries. Cell. Mol. Life Sci. 65, 1186–1201 (2008).

    Article  CAS  PubMed  Google Scholar 

  5. Nören-Müller, A. et al. Discovery of protein phosphatase inhibitor classes by biology-oriented synthesis. Proc. Natl. Acad. Sci. USA 103, 10606–10611 (2006).

    Article  PubMed  Google Scholar 

  6. Nören-Müller, A. et al. Discovery of a new class of inhibitors of Mycobacterium tuberculosis protein tyrosine phosphatase B by biology-oriented synthesis. Angew. Chem. Int. Ed. 47, 5973–5977 (2008).

    Article  Google Scholar 

  7. Schuffenhauer, A. et al. Clustering and rule-based classifications of chemical structures evaluated in the biological activity space. J. Chem. Inf. Model. 47, 325–336 (2007).

    Article  CAS  PubMed  Google Scholar 

  8. Johnson, A.M. & Maggiora, G.M. Concepts and Applications of Molecular Similarity (Wiley, New York, 1990).

    Google Scholar 

  9. Hajduk, P.J. & Greer, J. A decade of fragment-based drug design: strategic advances and lessons learned. Nat. Rev. Drug Discov. 6, 211–219 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Siegel, M.G. & Vieth, M. Drugs in other drugs: a new look at drugs as fragments. Drug Discov. Today 12, 71–79 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Olah, M. et al. WOMBAT: world of molecular bioactivity. in Chemoinformatics in Drug Discovery (ed. Oprea, T.I.) 223–239 (Wiley-VCH, Weinheim, Germany, 2004).

    Google Scholar 

  12. Olah, M. et al. WOMBAT and WOMBAT-PK: bioactivity databases for lead and drug discovery. in Chemical Biology: from Small Molecules to Systems Biology and Drug Design (eds. Schreiber, S.L., Kapoor, T.M. & Wess, G.) 760–786 (Wiley-VCH, Weinheim, Germany, 2007).

    Chapter  Google Scholar 

  13. Loewi, O. & Navratil, E. Humoral transmissability of the cardiac neural effect. XI. Announcement. The mechanism of the vagal effect of physostigmine and ergotamine. Pflugers Arch. Gesamte Physiol. Menschen Tiere 214, 689–696 (1936).

    Google Scholar 

  14. Schechter, I. & Berger, A. On the size of the active site in proteases. I. Papain. Biochem. Biophys. Res. Commun. 27, 157–162 (1967).

    Article  CAS  PubMed  Google Scholar 

  15. Ersmark, K., Del Valle, J.R. & Hanessian, S. Chemistry and biology of the aeruginosin family of serine protease inhibitors. Angew. Chem. Int. Edn Engl. 47, 1202–1223 (2008).

    Article  CAS  Google Scholar 

  16. Wiley, M.R. & Fisher, M.J. Small-molecule direct thrombin inhibitors. Expert Opin. Ther. Pat. 7, 1265–1282 (1997).

    Article  CAS  Google Scholar 

  17. Löwe, J., Li, H., Downing, K.H. & Nogales, E. Refined structure of alpha beta-tubulin at 3.5 A resolution. J. Mol. Biol. 313, 1045–1057 (2001).

    Article  PubMed  Google Scholar 

  18. Kingston, D.G.I. The shape of things to come: structural and synthetic studies of taxol and related compounds. Phytochemistry 68, 1844–1854 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. Schulz, W. Chemical Abstracts Service launches release 2.0 of innovative SciFinder. Chem. Eng. News 74, 43 (1996).

    CAS  Google Scholar 

  20. Staker, B.L. et al. The mechanism of topoisomerase I poisoning by a camptothecin analog. Proc. Natl. Acad. Sci. USA 99, 15387–15392 (2002).

    Article  CAS  PubMed  Google Scholar 

  21. Ducharme, Y. et al. Naphthalenic lignan lactones as selective, nonredox 5-lipoxygenase inhibitors - synthesis and biological-activity of (methoxyalkyl)thiazole and methoxytetrahydropyran hybrids. J. Med. Chem. 37, 512–518 (1994).

    Article  CAS  PubMed  Google Scholar 

  22. Hopkins, A.L., Groom, C.R. & Alex, A. Ligand efficiency: a useful metric for lead selection. Drug Discov. Today 9, 430–431 (2004).

    Article  PubMed  Google Scholar 

  23. Bourguet, W., Germain, P. & Gronemeyer, H. Nuclear receptor ligand-binding domains three-dimensional structures, molecular interactions and pharmacological implications. Trends Pharmacol. Sci. 21, 381–388 (2000).

    Article  CAS  PubMed  Google Scholar 

  24. Hoekstra, W.J. et al. Discovery of novel quinoline-based estrogen receptor ligands using peptide interaction profiling. J. Med. Chem. 48, 2243–2247 (2005).

    Article  CAS  PubMed  Google Scholar 

  25. Bologa, C.G. et al. Virtual and biomolecular screening converge on a selective agonist for GPR30. Nat. Chem. Biol. 2, 207–212 (2006).

    Article  CAS  PubMed  Google Scholar 

  26. Sadler, B.R. et al. Three-dimensional quantitative structure-activity relationship study of nonsteroidal estrogen receptor ligands using the comparative molecular field analysis cross-validated r(2)-guided region selection approach. J. Med. Chem. 41, 2261–2267 (1998).

    Article  CAS  PubMed  Google Scholar 

  27. Chesworth, R. et al. Tetrahydroisoquinolines as subtype selective estrogen agonists/antagonists. Bioorg. Med. Chem. Lett. 14, 2729–2733 (2004).

    Article  CAS  PubMed  Google Scholar 

  28. Hopkins, A.L., Mason, J.S. & Overington, J.P. Can we rationally design promiscuous drugs? Curr. Opin. Struct. Biol. 16, 127–136 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Nidhi, Glick, M., Davies, J.W. & Jenkins, J.L. Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J. Chem. Inf. Model. 46, 1124–1133 (2006).

    Article  CAS  PubMed  Google Scholar 

  30. Fattori, D. Molecular recognition: the fragment approach in lead generation. Drug Discov. Today 9, 229–238 (2004).

    Article  CAS  PubMed  Google Scholar 

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This research was supported by the Max-Planck-Gesellschaft and the Fonds der Chemischen Industrie. Part of this work was supported by US National Institutes of Health grants 1R01CA127731 and 1U54MH084690 (to T.I.O.) and by the German Federal Ministry for Education and Research through the German National Genome Research Network-Plus (grant number BMBF 01GS08102, to D.R. and H.W.). W.A.L.v.O. thanks the Alexander von Humboldt Foundation for a Georg Forster Research Fellowship for Experienced Researchers.

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Correspondence to Herbert Waldmann.

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T.I.O. is the owner of Sunset Molecular, the distributor of the WOMBAT database that we used in our studies.

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Renner, S., van Otterlo, W., Dominguez Seoane, M. et al. Bioactivity-guided mapping and navigation of chemical space. Nat Chem Biol 5, 585–592 (2009).

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