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Quantifying the chemical beauty of drugs

Abstract

Drug-likeness is a key consideration when selecting compounds during the early stages of drug discovery. However, evaluation of drug-likeness in absolute terms does not reflect adequately the whole spectrum of compound quality. More worryingly, widely used rules may inadvertently foster undesirable molecular property inflation as they permit the encroachment of rule-compliant compounds towards their boundaries. We propose a measure of drug-likeness based on the concept of desirability called the quantitative estimate of drug-likeness (QED). The empirical rationale of QED reflects the underlying distribution of molecular properties. QED is intuitive, transparent, straightforward to implement in many practical settings and allows compounds to be ranked by their relative merit. We extended the utility of QED by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds. The measure may also capture the abstract notion of aesthetics in medicinal chemistry.

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Figure 1: Histograms of eight selected molecular properties for a set of 771 orally absorbed small molecule drugs.
Figure 2: Benchmarking of QED against other measures of drug-likeness.
Figure 3: Chemical aesthetics.
Figure 4: Structural diversity networks.

References

  1. 1

    Keller, T. H., Pichota, A. & Yin, Z. A practical view of ‘druggability’. Curr. Opin. Chem. Biol. 10, 357–361 (2006).

    CAS  Article  Google Scholar 

  2. 2

    Ursu, O., Rayan, A., Goldblum, A. & Oprea, T. I. Understanding drug-likeness. Wiley Interdis. Rev.: Comp. Mol. Sci. 1, doi: 10.1002/wcms.1052 (2011).

  3. 3

    Oprea, T. I. Property distribution of drug-related chemical databases. J. Comput. Aided Mol. Des. 14, 251–264 (2000).

    CAS  Article  Google Scholar 

  4. 4

    Leeson, P. D. & Springthorpe, B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nature Rev. Drug Discov. 6, 881–890 (2007).

    CAS  Article  Google Scholar 

  5. 5

    Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Del. Rev. 23, 3–25 (1997).

    CAS  Article  Google Scholar 

  6. 6

    Lipinski, C. A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 3–25 (2000).

    Article  Google Scholar 

  7. 7

    Abad-Zapatero, C. A sorcerer's apprentice and The Rule of Five: from rule-of-thumb to commandment and beyond. Drug Discov. Today 12, 995–997 (2007).

    Article  Google Scholar 

  8. 8

    Hann, M. M. Molecular obesity, potency and other addictions in drug discovery. MedChemComm 2, 349–355 (2011).

    CAS  Article  Google Scholar 

  9. 9

    Hughes, J. D. et al. Physicochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett. 18, 4872–4875 (2008).

    CAS  Article  Google Scholar 

  10. 10

    Wenlock, M., Austin, R. P., Barton, P., Davis, A. M. & Leeson, P. D. A comparison of physiochemical property profiles of development and marketed oral drugs. J. Med. Chem. 46, 1250–1256 (2003).

    CAS  Article  Google Scholar 

  11. 11

    Proudfoot, J. R. The evolution of synthetic oral drug properties. Bioorg. Med. Chem. Lett. 15, 1087–1090 (2005).

    CAS  Article  Google Scholar 

  12. 12

    Xu, J. & Stevenson, J. Drug-like index: a new approach to measure drug-like compounds and their diversity. J. Chem. Inf. Comput. Sci. 40, 1177–1187 (2000).

    CAS  Article  Google Scholar 

  13. 13

    Rayan, A., Marcus, D. & Goldblum, A. Predicting oral druglikeness by iterative stochastic elimination. J. Chem. Info. Model. 50, 437–445 (2010).

    CAS  Article  Google Scholar 

  14. 14

    Ohno, K., Nagahara, Y., Tsunoyama, K. & Orita, M. Are there differences between launched drugs, clinical candidates, and commercially available compounds? J. Chem. Inf. Model. 50, 815–821 (2010).

    CAS  Article  Google Scholar 

  15. 15

    Harrington, E. C. Jr The desirability function. Ind. Qual. Control. 21, 494–498 (1965).

    Google Scholar 

  16. 16

    Cruz-Monteagudo, M. et al. Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries. J. Comb. Chem. 10, 897–913 (2008).

    CAS  Article  Google Scholar 

  17. 17

    Le Bailly de Tilleghem, C., Beck, B., Boulanger, B. & Govaerts, B. A fast exchange algorithm for designing focused libraries in lead optimization. J. Chem. Inf. Model. 45, 758–767 (2005).

    CAS  Article  Google Scholar 

  18. 18

    Mandal, A., Johnson, K., Wu, C. F. J. & Bornemeier, D. Identifying promising compounds in drug discovery: genetic algorithms and some new statistical techniques J. Chem. Inf. Model. 47, 981–988 (2007).

    CAS  Article  Google Scholar 

  19. 19

    Wager, T. T., Hou, X., Verhoest, P. R. & Villalobos, A. Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties. ACS Chemical Neurosci. 1, 435–449 (2010).

    CAS  Article  Google Scholar 

  20. 20

    Paolini, G. V., Lyons, R. & Laflin, P. How desirable are your IC50s? A method to enhance screening-based decision making. J. Biomol. Screen. 15, 1183–1193 (2010).

    CAS  Article  Google Scholar 

  21. 21

    Derringer, G. & Suich, R. Simultaneous optimization of several response variables. J. Qualty Technol. 12, 214–219 (1980).

    Article  Google Scholar 

  22. 22

    Ghose, A. K., Viswanadhan, V. N. & Wendoloski, J. J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem. 1, 55–68 (1999).

    CAS  Article  Google Scholar 

  23. 23

    Veber, D. F. et al. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45, 2615–2623 (2002).

    CAS  Article  Google Scholar 

  24. 24

    Ghose, A. K. & Crippen, G. M. J. Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure–activity relationships I. partition coefficients as a measure of hydrophobicity. J. Comput. Chem. 7, 565–577 (1986).

    CAS  Article  Google Scholar 

  25. 25

    Lovering, F., Bikker, J. & Humblet, C. Escape from flatland: increasing saturation as an approach to improving clinical success. J. Med. Chem. 52, 6752–6756 (2009).

    CAS  Article  Google Scholar 

  26. 26

    Ritchie, T. J. & Macdonald, S. J. The impact of aromatic ring count on compound developability – are too many aromatic rings a liability in drug design? Drug Discov. Today 14, 1011–1120 (2009).

    CAS  Article  Google Scholar 

  27. 27

    Brenk, R. et al. Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem 3, 435–444 (2008).

    CAS  Article  Google Scholar 

  28. 28

    Shannon, C. E. A mathematical theory of communication. Bell System Technical J. 27, 379–423, 623–656 (1948).

    Article  Google Scholar 

  29. 29

    Hosseinzadeh Lotfi, F. & Fallahnejad, R. Imprecise Shannon's entropy and multi attribute decision making. Entropy 12, 53–62 (2010).

    Article  Google Scholar 

  30. 30

    Wager, T. T. et al. Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes. ACS Chemical Neurosci. 1, 420–434 (2010).

    CAS  Article  Google Scholar 

  31. 31

    Gleeson, M. P., Hersey, A., Montanari, D. & Overington, J. Probing the links between in vitro potency, ADMET and physicochemical parameters. Nature Rev. Drug Discov. 10, 197–208 (2011).

    CAS  Article  Google Scholar 

  32. 32

    Knox, C. et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 39, D1035–D1041 (2011).

    CAS  Article  Google Scholar 

  33. 33

    ChEMBL https://www.ebi.ac.uk/chembldb/

  34. 34

    Takaoka, Y. et al. Development of a method for evaluating drug-likeness and ease of synthesis using a data set in which compounds are assigned scores based on chemists' intuition. J. Chem. Inf. Comput. Sci. 43, 1269–1275 (2003).

    CAS  Article  Google Scholar 

  35. 35

    Lajiness, M. S., Maggiora, G. M. & Shanmugasundaram, V. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J. Med. Chem. 47, 4891–4896 (2004).

    CAS  Article  Google Scholar 

  36. 36

    Muresan, S. & Sadowski, J. in Molecular Drug Properties – Measurement and Prediction (ed. Mannhold, R.) 441–457 (Wiley-VCH, 2008).

    Google Scholar 

  37. 37

    Lipinski, C. A. in Molecular Informatics: Confronting Complexity (eds Hicks, M. G. & Kettner, C.) (Beilstein-Institut, 2002).

    Google Scholar 

  38. 38

    Lipinski, C. A. Overview of hit to lead: the medicinal chemist's role from HTS retest to lead optimisation hand off. Top. Med. Chem. 5, 1–24 (2009).

    Article  Google Scholar 

  39. 39

    Wipke, W. T. & Rogers, D. Artificial intelligence in organic synthesis. SST: starting material selection strategies. An application of superstructure search. J. Chem. Inf. Comput. Sci. 24, 71–81 (1984).

    CAS  Article  Google Scholar 

  40. 40

    Hopkins, A. L. & Groom, C. R. The druggable genome. Nature Rev. Drug Discov. 1, 727–730 (2002).

    CAS  Article  Google Scholar 

  41. 41

    An, J., Totrov, M. & Abagyan, R. Comprehensive identification of ‘druggable’ protein ligand binding sites. Genome Inform. 15, 31–41 (2004).

    CAS  PubMed  Google Scholar 

  42. 42

    Cheng, A. C. et al. Structure-based maximal affinity model predicts small-molecule druggability. Nature Biotechnol. 25, 71–75 (2007).

    Article  Google Scholar 

  43. 43

    Halgren, T. A. Identifying and characterizing binding sites and assessing druggability. J. Chem. Inf. Model. 49, 377–389 (2009).

    CAS  Article  Google Scholar 

  44. 44

    Schmidtke, P. & Barril, X. Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J. Med. Chem. 53, 5858–5867 (2010).

    CAS  Article  Google Scholar 

  45. 45

    Southan, C., Boppana, K., Jagarlapudi, S. A. & Muresan, S. Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: ranking 1,654 human protein targets by assayed compounds and molecular scaffolds. J. Cheminform. 3, 14 (2011).

    Article  Google Scholar 

  46. 46

    Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nature Rev. Drug Discov. 5, 993–996 (2006).

    CAS  Article  Google Scholar 

  47. 47

    Manchester, J., Walkup, G., Rivin, O. & You, Z. Evaluation of pKa estimation methods on 211 druglike compounds. J. Chem. Inf. Model. 50, 565–571 (2010).

    CAS  Article  Google Scholar 

  48. 48

    Shimazaki, H. & Shinomoto, S. in Advances in Neural Information Processing Systems Vol. 19 (eds Schölkopf, B., Platt, J. & Hoffman, T.) 1289–1296 (MIT Press, 2007).

    Google Scholar 

  49. 49

    Dimitropoulos, D., Ionides, J. and Henrick, K. in Current Protocols in Bioinformatics (eds Baxevanis, A. D., Page, R. D. M., Petsko, G. A., Stein, L. D. & Stormo, G. D.) 14.13.11–14.13.13 (Wiley, 2006).

    Google Scholar 

  50. 50

    Gleeson, M. P. Generation of a set of simple, interpretable ADMET rules of thumb. J. Med. Chem. 51, 817–834 (2008).

    CAS  Article  Google Scholar 

  51. 51

    Congreve, M., Carr, R., Murray, C. & Jhoti, H. A ‘rule of three’ for fragment-based lead discovery? Drug Discov. Today 8, 876–877 (2003).

    Article  Google Scholar 

  52. 52

    Luker, T. et al. Strategies to improve in vivo toxicology outcomes for basic candidate drug molecules. Bioorg. Med. Chem. Lett. 21, 5673–5679 (2011).

    CAS  Article  Google Scholar 

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Acknowledgements

This research received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement N° 223461 and the Scottish Universities Life Sciences Alliance. We thank J. Overington for providing the DrugStore data, R. Brenk for the provision of SMARTS for structural alerts and I. Carruthers for assistance with DrugStore database queries. We also thank the chemistry community of AstraZeneca for participating in the chemistry survey.

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A.L.H. conceived the approach and designed the algorithm. G.R.B. implemented the algorithm, performed the calculations, identified the functions and performed the analysis. G.V.P. developed the use of Shannon entropy as the weighting scheme, J.B. wrote the Pipeline Pilot implementation, S.M. coordinated the survey of AstraZeneca chemists, A.L.H and G.R.B. co-wrote the manuscript and G.R.B. produced the figures. All authors commented on the manuscript.

Corresponding author

Correspondence to Andrew L. Hopkins.

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The authors declare no competing financial interests.

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Bickerton, G., Paolini, G., Besnard, J. et al. Quantifying the chemical beauty of drugs. Nature Chem 4, 90–98 (2012). https://doi.org/10.1038/nchem.1243

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