Quantifying the chemical beauty of drugs (original) (raw)

References

  1. Keller, T. H., Pichota, A. & Yin, Z. A practical view of ‘druggability’. Curr. Opin. Chem. Biol. 10, 357–361 (2006).
    Article CAS Google Scholar
  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. Oprea, T. I. Property distribution of drug-related chemical databases. J. Comput. Aided Mol. Des. 14, 251–264 (2000).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  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. 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. Hann, M. M. Molecular obesity, potency and other addictions in drug discovery. MedChemComm 2, 349–355 (2011).
    Article CAS Google Scholar
  9. Hughes, J. D. et al. Physicochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett. 18, 4872–4875 (2008).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  11. Proudfoot, J. R. The evolution of synthetic oral drug properties. Bioorg. Med. Chem. Lett. 15, 1087–1090 (2005).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  13. Rayan, A., Marcus, D. & Goldblum, A. Predicting oral druglikeness by iterative stochastic elimination. J. Chem. Info. Model. 50, 437–445 (2010).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  15. Harrington, E. C. Jr The desirability function. Ind. Qual. Control. 21, 494–498 (1965).
    Google Scholar
  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).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  21. Derringer, G. & Suich, R. Simultaneous optimization of several response variables. J. Qualty Technol. 12, 214–219 (1980).
    Article Google Scholar
  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).
    Article CAS Google Scholar
  23. Veber, D. F. et al. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45, 2615–2623 (2002).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  27. Brenk, R. et al. Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem 3, 435–444 (2008).
    Article CAS Google Scholar
  28. Shannon, C. E. A mathematical theory of communication. Bell System Technical J. 27, 379–423, 623–656 (1948).
    Article Google Scholar
  29. Hosseinzadeh Lotfi, F. & Fallahnejad, R. Imprecise Shannon's entropy and multi attribute decision making. Entropy 12, 53–62 (2010).
    Article Google Scholar
  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).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  32. Knox, C. et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 39, D1035–D1041 (2011).
    Article CAS Google Scholar
  33. ChEMBL https://www.ebi.ac.uk/chembldb/
  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).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  36. Muresan, S. & Sadowski, J. in Molecular Drug Properties – Measurement and Prediction (ed. Mannhold, R.) 441–457 (Wiley-VCH, 2008).
    Google Scholar
  37. Lipinski, C. A. in Molecular Informatics: Confronting Complexity (eds Hicks, M. G. & Kettner, C.) (Beilstein-Institut, 2002).
    Google Scholar
  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. 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).
    Article CAS Google Scholar
  40. Hopkins, A. L. & Groom, C. R. The druggable genome. Nature Rev. Drug Discov. 1, 727–730 (2002).
    Article CAS Google Scholar
  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. Cheng, A. C. et al. Structure-based maximal affinity model predicts small-molecule druggability. Nature Biotechnol. 25, 71–75 (2007).
    Article Google Scholar
  43. Halgren, T. A. Identifying and characterizing binding sites and assessing druggability. J. Chem. Inf. Model. 49, 377–389 (2009).
    Article CAS Google Scholar
  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).
    Article CAS Google Scholar
  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. Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nature Rev. Drug Discov. 5, 993–996 (2006).
    Article CAS Google Scholar
  47. Manchester, J., Walkup, G., Rivin, O. & You, Z. Evaluation of p_K_a estimation methods on 211 druglike compounds. J. Chem. Inf. Model. 50, 565–571 (2010).
    Article CAS Google Scholar
  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. 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. Gleeson, M. P. Generation of a set of simple, interpretable ADMET rules of thumb. J. Med. Chem. 51, 817–834 (2008).
    Article CAS Google Scholar
  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. Luker, T. et al. Strategies to improve in vivo toxicology outcomes for basic candidate drug molecules. Bioorg. Med. Chem. Lett. 21, 5673–5679 (2011).
    Article CAS Google Scholar

Download references