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Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account

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Abstract

Activity cliffs are formed by pairs or groups of structurally similar compounds with significant differences in potency. They represent a prominent feature of activity landscapes of compound data sets and a primary source of structure–activity relationship (SAR) information. Thus far, activity cliffs have only been considered for active compounds, consistent with the principles of the activity landscape concept. However, from an SAR perspective, pairs formed by structurally similar active and inactive compounds should often also be informative. Therefore, we have extended the activity cliff concept to also take inactive compounds into consideration. As source of both confirmed active and inactive compounds, we have exclusively focused on PubChem confirmatory bioassays. Activity cliffs formed between pairs of active compounds (homogeneous pairs) and pairs of active and inactive compounds (heterogeneous pairs) were systematically analyzed on a per-assay basis, hence ensuring the currently highest possible degree of experimental consistency in activity measurement. Only very small numbers of large-magnitude activity cliffs formed between active compounds were detected in PubChem bioassays. However, when taking confirmed inactive compounds from confirmatory assays into account, the activity cliff frequency in assay data significantly increased, involving 11–15 % of all qualifying pairs of similar compounds, depending on the molecular representations that were used. Hence, these non-conventional activity cliffs provide an additional source of SAR information.

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References

  1. Maggiora GM (2006) On outliers and activity cliffs—why QSAR often disappoints. J Chem Inf Model 46(4):1535

    Article  CAS  Google Scholar 

  2. Stumpfe D, Bajorath J (2012) Exploring activity cliffs in medicinal chemistry. J Med Chem 55(7):2932–2942

    Article  CAS  Google Scholar 

  3. Guha R, Van Drie JH (2008) Structure-activity landscape index: identifying and quantifying activity cliffs. J Chem Inf Model 48(3):646–658

    Article  CAS  Google Scholar 

  4. Wassermann AM, Dimova D, Bajorath J (2011) Comprehensive analysis of single- and multi-target activity cliffs formed by currently available bioactive compounds. Chem Biol Drug Des 78(2):224–228

    Article  CAS  Google Scholar 

  5. Wassermann AM, Wawer M, Bajorath J (2010) Activity landscape representations for structure—activity relationship analysis. J Med Chem 53(23):8209–8223

    Article  CAS  Google Scholar 

  6. Medina-Franco JL, Martínez-Mayorga K, Bender A, Marín RM, Giulianotti MA, Pinilla C, Houghten RA (2009) Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs. J Chem Inf Model 49(2):477–491

    Article  CAS  Google Scholar 

  7. Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model 50(3):339–348

    Article  CAS  Google Scholar 

  8. Hu X, Hu Y, Vogt M, Stumpfe D, Bajorath J (2012) MMP-cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs. J Chem Inf Model 52(5):1138–1145

    Article  CAS  Google Scholar 

  9. Hu Y, Bajorath J (2012) Extending the activity cliff concept: structural categorization of activity cliffs and systematic identification of different types of cliffs in the ChEMBL database. J Chem Inf Model 52(7):1806–1811

    Article  CAS  Google Scholar 

  10. Seebeck B, Wagener M, Rarey M (2011) From activity cliffs to target-specific scoring models and pharmacophore hypotheses. ChemMedChem 6(9):1630–1639

    Article  CAS  Google Scholar 

  11. Hu Y, Bajorath J (2012) Exploration of 3D activity cliffs on the basis of compound binding modes and comparison of 2D and 3D cliffs. J Chem Inf Model 52(3):670–677

    Article  CAS  Google Scholar 

  12. Hu Y, Furtmann N, Gütschow M, Bajorath J (2012) Systematic identification and classification of three-dimensional activity cliffs. J Chem Inf Model 52(6):1490–1498

    Article  CAS  Google Scholar 

  13. Berman H, Henrick K, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242

    Article  CAS  Google Scholar 

  14. Vogt M, Huang Y, Bajorath J (2011) From activity cliffs to activity ridges: informative data structures for SAR analysis. J Chem Inf Model 51(8):1848–1856

    Article  CAS  Google Scholar 

  15. Namasivayam V, Bajorath J (2012) Searching for coordinated activity cliffs using particle swarm optimization. J Chem Inf Model 52(4):927–934

    Article  CAS  Google Scholar 

  16. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35(Database issue):D198–D201

    Article  CAS  Google Scholar 

  17. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(Database issue):D1100–D1107

    Article  CAS  Google Scholar 

  18. Stumpfe D, Bajorath J (2012) Frequency of occurrence and potency range distribution of activity cliffs in bioactive compounds. J Chem Inf Model 52(9):2348–2353

    Article  CAS  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 (2012) PubChem’s bioassay database. Nucleic Acids Res 40(Database issue):D400–D412

    Article  CAS  Google Scholar 

  20. MACCS Structural Keys, Symyx Software: San Ramon, CA, 2005

  21. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742–754

    Article  CAS  Google Scholar 

  22. Willett P (2005) Searching techniques for databases of two- and three-dimensional structures. J Med Chem 48(13):4183–4199

    Article  CAS  Google Scholar 

  23. Wawer M, Bajorath J (2010) Similarity-potency trees: a method to search for SAR information in compound data sets and derive SAR rules. J Chem Inf Model 50(8):1395–1409

    Article  CAS  Google Scholar 

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Correspondence to Jürgen Bajorath.

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Hu, Y., Maggiora, G.M. & Bajorath, J. Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account. J Comput Aided Mol Des 27, 115–124 (2013). https://doi.org/10.1007/s10822-012-9632-4

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  • DOI: https://doi.org/10.1007/s10822-012-9632-4

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