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Enhancing Molecular Promiscuity Evaluation Through Assay Profiles

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The growing amount of heterogeneous bioactivity data requires effective strategies to assess the promiscuity/selectivity of small-molecules and aid drug discovery. In the current study, we aim to evaluate the potential of assay profiles (APs, i.e., unique combinations of assay-related features describing how activity determinations were performed and reported) in molecular promiscuity analysis.


Using PubChem bioactivity data, we computed for all Molecular Libraries Small Molecule Repository (MLSMR library) compounds the frequency of hits score (FoH, i.e., the ratio between the number of times the compound was found active and the number of times it was tested), which were subsequently fit into 32 theoretical APs. The promiscuity of drugs and non-drugs was compared at different levels of test results.


We found 8 dominant APs, indicating that compounds tested in more than ten assays (or against ten targets) and found active at least once tend to reach near to maximum hit rates in scientific literature and confirmatory assays (e.g., 95% of the drugs show FoH scores >0.93). Primary and high-throughput screening testing results in very low hit rates (e.g., 95% of the compounds show FoH scores <0.11), promoting a different perspective of promiscuity. In general, drugs exert higher promiscuity compared to non-drugs. Targets and classes of drugs are also discussed within the main APs.


APs contain relevant features and are suited for big data promiscuity analysis. The activity data of the main APs are freely available on

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PubChem bioassay identification


Assay profiles


PubChem compound identification


Frequency of hits score


Frequency of hits score


High-throughput screening


Promiscuity degree


Scientific literature


Number of test results


  1. Simeonov A, Jadhav A, Thomas CJ, Wang Y, Huang R, Southall NT, et al. Fluorescence spectroscopic profiling of compound libraries. J Med Chem. 2008;51(8):2363–71.

    Article  CAS  Google Scholar 

  2. Hopkins AL, Mason JS, Overington JP. Can we rationally design promiscuous drugs? Curr Opin Struct Biol. 2006;16(1):127–36.

    Article  CAS  Google Scholar 

  3. 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.

    Article  CAS  Google Scholar 

  4. Jadhav A, Ferreira RS, Klumpp C, Mott BT, Austin CP, Inglese J, et al. Quantitative analyses of aggregation, autofluorescence, and reactivity artifacts in a screen for inhibitors of a thiol protease. J Med Chem. 2010;53(1):37–51.

    Article  CAS  Google Scholar 

  5. Seidler J, McGovern SL, Doman TN, Shoichet BK. Identification and prediction of promiscuous aggregating inhibitors among known drugs. J Med Chem. 2003;46(21):4477–86.

    Article  CAS  Google Scholar 

  6. McGovern SL, Helfand BT, Feng B, Shoichet BK. A specific mechanism of nonspecific inhibition. J Med Chem. 2003;46(20):4265–72.

    Article  CAS  Google Scholar 

  7. McGovern SL, Caselli E, Grigorieff N, Shoichet BK. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J Med Chem. 2002;45(8):1712–22.

    Article  CAS  Google Scholar 

  8. Austin CP, Brady LS, Insel TR, Collins FS. NIH Molecular Libraries Initiative. Science. 2004;306(5699):1138–9.

    Article  CAS  Google Scholar 

  9. Auld DS, Southall NT, Jadhav A, Johnson RL, Diller DJ, Simeonov A, et al. Characterization of chemical libraries for luciferase inhibitory activity. J Med Chem. 2008;51(8):2372–86.

    Article  CAS  Google Scholar 

  10. Jasial S, Hu Y, Bajorath J. Determining the degree of promiscuity of extensively assayed compounds. PLoS One. 2016;11(4):e0153873.

    Article  Google Scholar 

  11. Gilberg E, Jasial S, Stumpfe D, Dimova D, Bajorath J. Highly promiscuous small molecules from biological screening assays include many pan-assay interference compounds but also candidates for polypharmacology. J Med Chem. 2016;59(22):10285–90.

    Article  CAS  Google Scholar 

  12. Hu Y, Gupta-Ostermann D, Bajorath J. Exploring compound promiscuity patterns and multi-target activity spaces. Comput Struct Biotechnol J. 2014;9:e201401003.

    Article  Google Scholar 

  13. Hu Y, Bajorath J. High-resolution view of compound promiscuity. F1000Res. 2013;2:144.

    PubMed  PubMed Central  Google Scholar 

  14. Hu Y, Bajorath J. Compound promiscuity: what can we learn from current data? Drug Discov Today. 2013;18(13–14):644–50.

    Article  CAS  Google Scholar 

  15. Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, et al. The ChEMBL database in 2017. Nucleic Acids Res. 2017;45(D1):D945–D54.

    Article  CAS  Google Scholar 

  16. Wang Y, Bryant SH, Cheng T, Wang J, Gindulyte A, Shoemaker BA, et al. PubChem BioAssay: 2017 update. Nucleic Acids Res. 2017;45(D1):D955–D63.

    Article  CAS  Google Scholar 

  17. Dahlin JL, Nissink JW, Strasser JM, Francis S, Higgins L, Zhou H, et al. PAINS in the assay: chemical mechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS. J Med Chem. 2015;58(5):2091–113.

    Article  CAS  Google Scholar 

  18. Whitty A. Growing PAINS in academic drug discovery. Future Med Chem. 2011;3(7):797–801.

    Article  CAS  Google Scholar 

  19. Azzaoui K, Hamon J, Faller B, Whitebread S, Jacoby E, Bender A, et al. Modeling promiscuity based on in vitro safety pharmacology profiling data. ChemMedChem. 2007;2(6):874–80.

    Article  CAS  Google Scholar 

  20. Yang JJ, Ursu O, Lipinski CA, Sklar LA, Oprea TI, Bologa CG. Badapple: promiscuity patterns from noisy evidence. J Cheminform. 2016;8:29.

    Article  CAS  Google Scholar 

  21. Avram SI, Pacureanu LM, Bora A, Crisan L, Avram S, Kurunczi L. ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes. J Chem Inf Model. 2014;54(8):2360–70.

    Article  CAS  Google Scholar 

  22. Hu Y, Bajorath J. Entering the ‘big data’ era in medicinal chemistry: molecular promiscuity analysis revisited. Future Sci OA 2017;3(2):FSO179.

    Article  Google Scholar 

  23. Hu Y, Jasial S, Gilberg E, Bajorath J. Structure-promiscuity relationship puzzles-extensively assayed analogs with large differences in target annotations. AAPS J. 2017;19(3):856–64.

    Article  Google Scholar 

  24. Hu Y, Bajorath J. What is the likelihood of an active compound to be promiscuous? Systematic assessment of compound promiscuity on the basis of PubChem confirmatory bioassay data. AAPS J. 2013;15(3):808–15.

    Article  CAS  Google Scholar 

  25. Hu Y, Bajorath J. How promiscuous are pharmaceutically relevant compounds? A data-driven assessment. AAPS J. 2013;15(1):104–11.

    Article  CAS  Google Scholar 

  26. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, et al. PubChem substance and compound databases. Nucleic Acids Res. 2016;44(D1):D1202–13.

    Article  CAS  Google Scholar 

  27. PubChem database. 2016. Available from:

  28. Schreiber SL, Kotz JD, Li M, Aube J, Austin CP, Reed JC, et al. Advancing biological understanding and therapeutics discovery with small-molecule probes. Cell. 2015;161(6):1252–65.

    Article  CAS  Google Scholar 

  29. ChEMBL version 22_1 ( 2017. Available from:

  30. ChemAxon JChem API package, version, ChemAxon Ltd. 2016. Available from:

  31. National Center for Biotechnology Information. 2016. Available from:

  32. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7:539.

    Article  Google Scholar 

  33. Murtagh F, Legendre P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J Classif. 2014;31(3):274–95.

    Article  Google Scholar 

  34. R: A Language and Environment for Statistical Computing, version 3.2.3. 2015. Available from: http://www.R-projectorg/

  35. UniProt Consortium. Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2012;40:D71–5.

    Article  Google Scholar 

  36. Avram S, Bora A, Halip L, Curpan R. Modeling Kinase Inhibition Using Highly Confident Data Sets. J Chem Inf Model. 2018;58(5):957–67.

    Article  CAS  Google Scholar 

  37. Zhang X, Crespo A, Fernandez A. Turning promiscuous kinase inhibitors into safer drugs. Trends Biotechnol. 2008;26(6):295–301.

    Article  Google Scholar 

  38. Davis MI, Hunt JP, Herrgard S, Ciceri P, Wodicka LM, Pallares G, et al. Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol. 2011;29(11):1046–51.

    Article  CAS  Google Scholar 

  39. Metz JT, Johnson EF, Soni NB, Merta PJ, Kifle L, Hajduk PJ. Navigating the kinome. Nat Chem Biol. 2011;7(4):200–2.

    Article  CAS  Google Scholar 

  40. Anastassiadis T, Deacon SW, Devarajan K, Ma H, Peterson JR. Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity. Nat Biotechnol. 2011;29(11):1039–45.

    Article  CAS  Google Scholar 

  41. Venny, OJ. An interactive tool for comparing lists with Venn’s diagrams. 2007–2015. Available from:

  42. Senger MR, Fraga CA, Dantas RF, Silva FP Jr. Filtering promiscuous compounds in early drug discovery: is it a good idea? Drug Discov Today. 2016;21(6):868–72.

    Article  CAS  Google Scholar 

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Acknowledgments and Disclosures

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS−UEFISCDI, project number PN-II-RU-TE-2014-4-0422 and Romanian Academy, Institute of Chemistry Timişoara, project number 1.2.4/2018. All of the authors are indebted to ChemAxon Ltd. for providing access to their software.

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Correspondence to Sorin Avram or Liliana Halip.

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Avram, S., Curpan, R., Bora, A. et al. Enhancing Molecular Promiscuity Evaluation Through Assay Profiles. Pharm Res 35, 240 (2018).

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