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Structure-Promiscuity Relationship Puzzles—Extensively Assayed Analogs with Large Differences in Target Annotations

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Abstract

Publicly available screening data were systematically searched for extensively assayed structural analogs with large differences in the number of targets they were active against. Screening compounds with potential chemical liabilities that may give rise to assay artifacts were identified and excluded from the analysis. “Promiscuity cliffs” were frequently identified, defined here as pairs of structural analogs with a difference of at least 20 target annotations across all assays they were tested in. New assay indices were introduced to prioritize cliffs formed by screening compounds that were extensively tested in comparably large numbers of assays including many shared assays. In these cases, large differences in promiscuity degrees were not attributable to differences in assay frequency and/or lack of assay overlap. Such analog pairs have high priority for further exploring molecular origins of multi-target activities. Therefore, these promiscuity cliffs and associated target annotations are made freely available. The corresponding analogs often represent equally puzzling and interesting examples of structure-promiscuity relationships.

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References

  1. Johnson M, Maggiora GM, editors. Concepts and applications of molecular similarity. John Wiley & Sons: New York; 1990.

    Google Scholar 

  2. Wassermann AM, Wawer M, Bajorath J. Activity landscape representations for structure-activity relationship analysis. J Med Chem. 2010;53(23):8209–23. doi:10.1021/jm100933w.

    Article  CAS  PubMed  Google Scholar 

  3. Stumpfe D, Hu Y, Dimova D, Bajorath J. Recent progress in understanding activity cliffs and their utility in medicinal chemistry. J Med Chem. 2014;57(1):18–28. doi:10.1021/jm401120g.

    Article  CAS  PubMed  Google Scholar 

  4. Bredel M, Jacoby E. Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Genet. 2004;5(4):262–75. doi:10.1038/nrg1317.

    Article  CAS  PubMed  Google Scholar 

  5. Hu Y, Bajorath J. Compound promiscuity—what can we learn from current data. Drug Discov Today. 2013;18(13–14):644–50. doi:10.1016/j.drudis.2013.03.002.

    Article  CAS  PubMed  Google Scholar 

  6. Hu Y, Bajorath J. High-resolution view of compound promiscuity. F1000Research. 2013;2:144. doi:10.12688/f1000research.2-144.v2.

    PubMed  PubMed Central  Google Scholar 

  7. Paolini GV, Shapland RH, van Hoorn WP, Mason JS, Hopkins AL. Global mapping of pharmacological space. Nat Biotechnol. 2006;24(7):805–15. doi:10.1038/nbt1228.

    Article  CAS  PubMed  Google Scholar 

  8. Boran AD, Iyengar R. Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Devel. 2010;13(3):297–309.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Roukos DH. Network medicine: from reductionism to evidence of complex biomolecular interactions. Pharmacogenomics. 2011;12(5):695–8. doi:10.2217/pgs.11.28.

    Article  PubMed  Google Scholar 

  10. Nurse P. Reductionism: the ends of understanding. Nature. 1997;387(6634):657.

    Article  CAS  PubMed  Google Scholar 

  11. Bruns RF, Watson IA. Rules for identifying potentially reactive or promiscuous compounds. J Med Chem. 2012;55(22):9763–72. doi:10.1021/jm301008n.

    Article  CAS  PubMed  Google Scholar 

  12. Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53(7):2719–40. doi:10.1021/jm901137j.

    Article  CAS  PubMed  Google Scholar 

  13. Baell J, Walters MA. Chemical con artists foil drug discovery. Nature. 2014;513(7519):481–3. doi:10.1038/513481a.

    Article  CAS  PubMed  Google Scholar 

  14. McGovern SL, Caselli E, Grigorieff NA. Common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J Med Chem. 1996;45(8):1712–22. doi:10.1021/jm010533y.

    Article  Google Scholar 

  15. Shoichet BK. Screening in a spirit haunted world. Drug Discov Today. 2006;11(13–14):607–15. doi:10.1016/j.drudis.2006.05.014.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dimova D, Hu Y, Bajorath J. Matched molecular pair analysis of small molecule microarray data identified promiscuity cliffs and identifies molecular origins of extreme compound promiscuity. J Med Chem. 2012;55(22):10220–8. doi:10.1021/jm301292a.

    Article  CAS  PubMed  Google Scholar 

  17. Dimova D, Gilberg E, Bajorath J. Identification and analysis of promiscuity cliffs formed by bioactive compounds and experimental implications. RSC Adv. 2017;7:58–66. doi:10.1039/C6RA27247A.

    Article  CAS  Google Scholar 

  18. Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, Krüger FA, Light Y, Mak L, McGlinchey S, Nowotka M, Papadatos G, Santos R, Overington JP. The ChEMBL bioactivity database: an update. Nucleic Acids Res. 2014;42(Database issue):D1083–90. doi:10.1093/nar/gkt1031.

    Article  CAS  PubMed  Google Scholar 

  19. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker BA, Bolton E, Gindulyte A, Bryant SH. PubChem’s BioAssay database. Nucleic Acids Res. 2012;40(Database issue):D400–12. doi:10.1093/nar/gkr1132.

    Article  CAS  PubMed  Google Scholar 

  20. Jasial S, Hu Y, Bajorath J. Determining the degree of promiscuity of extensively assayed compounds. PLoS One. 2016;11(4):e0153873. doi:10.1371/journal.pone.0153873.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Griffen E, Leach AG, Robb GR, Warner DJ. Matched molecular pairs as a medicinal chemistry tool. J Med Chem. 2011;54(22):7739–50. doi:10.1021/jm200452d.

    Article  CAS  PubMed  Google Scholar 

  22. Hussain J, Rea C. Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model. 2010;50(3):339–48. doi:10.1021/ci900450m.

    Article  CAS  PubMed  Google Scholar 

  23. Hu X, Hu Y, Vogt M, Stumpfe D, Bajorath J. MMP-cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs. J Chem Inf Model. 2012;52(5):1138–45. doi:10.1021/ci3001138.

    Article  CAS  PubMed  Google Scholar 

  24. OEChem, version 1.7.7, OpenEye Scientific Software, Inc., Santa Fe, NM, USA. 2012.

  25. RDKit, Cheminformatics and Machine Learning Software, 2013. http://www.rdkit.org.

  26. Sterling T, Irwin JJ. ZINC 15—ligand discovery for everyone. J Chem Inf Model. 2015;55(11):2324–37. doi:10.1021/acs.jcim.5b00559.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Lagorce D, Sperandio O, Baell JB, Miteva MA, Villoutreix BO. FAF-Drugs3: a web server for compound property calculation and chemical library design. Nucleic Acids Res. 2015;43(W1):W200–7. doi:10.1093/nar/gkv353.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Irwin JJ, Duan D, Torosyan H, Doak AK, Ziebart KT, Sterling T, Tumanian G, Shoichet BK. An aggregation advisor for ligand discovery. J Med Chem. 2015;58(17):7076–87. doi:10.1021/acs.jmedchem.5b01105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Willett P, Barnard J, Downs GM. Chemical similarity searching. J Chem Inf Comput Sci. 1998;38(6):983–96. doi:10.1021/ci9800211.

    Article  CAS  Google Scholar 

  30. UniProtConsortium. The universal protein resource (UniProt) in 2010. Nucleic Acids Res. 2010;38(Database issue):D142–8. doi:10.1093/nar/gkp846.

    Article  Google Scholar 

  31. Müller G. Medicinal chemistry of target family-directed masterkeys. Drug Discov Today. 2003;8(15):681–91. doi:10.1016/S1359-6446(03)02781-8.

    Article  PubMed  Google Scholar 

  32. Hu Y, Jasial S, Gilberg E, Bajorath J. High-priority promiscuity cliffs from PubChem. Zenodo. 2017. doi:10.5281/zenodo.321285.

  33. Wassermann AM, Bajorath J. Chemical substitutions that introduce activity cliffs across different compound classes and biological targets. J Chem Inf Model. 2010;50(7):1248–56. doi:10.1021/ci1001845.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank OpenEye Scientific Software, Inc. for a free academic license of the OpenEye Toolkits.

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Correspondence to Jürgen Bajorath.

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Hu, Y., Jasial, S., Gilberg, E. et al. Structure-Promiscuity Relationship Puzzles—Extensively Assayed Analogs with Large Differences in Target Annotations. AAPS J 19, 856–864 (2017). https://doi.org/10.1208/s12248-017-0066-8

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