Abstract
The concept of ligand potency is briefly discussed here as well as why this is still a challenge for its complete comprehension. In this context, we explain also the meaning of ligand efficiency (LE), which has been greeted with both enthusiasm and criticism among the drug design audience. A full understanding of LE requires the complex interpretation of the potency concept presenting the uncertainty similar to this of the Zeno paradox. In reality, the efficiency of LE is caused by the high degree of preference for slim pharma drug candidates.
Keywords: Ligand efficiency, potency, biological activity, Big Data, drug design, PubChem, ChEMBL.
Graphical Abstract
[1]
Hill, A.V. The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. In: The Journal of
Physiology; Langley, Ed.; Cambridge University Press: London;
1910; Vol. 40, pp. iv-vi.
[2]
Colquhoun, D. The quantitative analysis of drug–receptor interactions: A short history. Trends Pharmacol. Sci., 2006, 27, 149-157.
[4]
Leake, M. The physics of life: one molecule at a time. Philos. Trans. R. Soc. Lond. B Biol. Sci., 2013, 368, 1611.
[5]
Bensaude-Vincent, B.; Simon, J. Chemistry — The Impure Science, 2nd ed; Imperial College Press: London, 2012.
[6]
Polanski, J. Chemoinformatics: From Chemical Art to Chemistry.
in Silico Encyclopedia of Bioinformatics and Computational Biology;
Ranganathan, Ed.; Elsevier; 2019; Vol. 2, pp. 601-618.
[7]
Polanski, J.; Gasteiger, J. Computer Representation of Chemical
Compounds. In: Handbook of Computational Chemistry; Leszczynski,
Ed.; Springer: Dordrecht; 2016, pp. 1-43.
[8]
Rosenblum, B.; Kuttner, F. Quantum Enigma: Physics Encounters Consciousness, 1st ed; Oxford University Press: New York, 2006.
[9]
Polanski, J.; Tkocz, A. Between descriptors and properties: understanding the ligand efficiency trends for G protein-coupled receptor and kinase structure-activity data sets. J. Chem. Inf. Model., 2017, 57(6), 1321-1329.
[10]
Polanski, J.; Tkocz, A.; Kucia, U. Beware of ligand efficiency (LE): Understanding LE data in modeling structure-activity and structure-economy relationships. J. Cheminformatics., 2017, 9, 49.
[11]
Ginsberg, J.; Mohebbi, M.; Patel, R.; Brammer, L.; Smolinski, M.; Brilliant, L. Detecting influenza epidemics using search engine query data. Nature, 2009, 457(7232), 1012-1014.
[12]
Polanski, J. Big Data in Structure-Property Studies-From Definitions to Models. In: Advances in QSAR Modeling; Roy Ed.;
Springer: Cham, 2017, pp. 529-555.
[13]
Aldrich, C.; Bertozzi, C.; Georg, G.; Kiessling, L.; Lindsley, C.; Liotta, D.; Merz, K.; Schepartz, A.; Wang, S. The ecstasy and agony of assay interference compounds. ACS Cent. Sci., 2017, 3(3), 143-147.
[14]
Gohlke, H.; Klebe, G. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem. Int. Ed. Engl., 2002, 41(15), 2645-2676.
[15]
Southan, C. Caveat USOR: Assessing differences between major chemistry databases. ChemMedChem, 2018, 13(6), 470-481.
[16]
Hann, M.; Leach, A.; Harper, G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J. Chem. Inf. Comput. Sci., 2001, 41(3), 856-864.
[17]
Zartler, E.; Shapiro, M. Fragonomics: fragment-based drug discovery. Curr. Opin. Chem. Biol., 2005, 9(4), 366-370.
[18]
Walters, W.P.; Green, J.; Weiss, J.; Murcko, M. What do medicinal chemists actually make? A 50-year retrospective. J. Med. Chem., 2011, 54(19), 6405-6416.
[19]
Gleeson, M.P.; Hersey, A.; Montanari, D.; Overington, J. Probing the links between in vitro potency, ADMET and physicochemical parameters. Nat. Rev. Drug Discov., 2011, 10(3), 197-208.
[20]
Kuntz, I.D.; Chen, K.; Sharp, K.A.; Kollman, P.A. The maximal affinity of ligands. Proc. Natl. Acad. Sci. USA, 1999, 96(18), 9997-10002.
[21]
Hopkins, A.; Keseru, G.; Leeson, P.; Rees, D.; Reynolds, C. The role of ligand efficiency metrics in drug discovery. Nat. Rev. Drug Discov., 2014, 13(2), 105-121.
[22]
Murray, C.; Erlanson, D.; Hopkins, A.; Keseru, G.; Leeson, P.; Rees, D.; Reynolds, C.; Richmond, N. Validity of ligand efficiency metrics. ACS Med. Chem. Lett., 2014, 5(6), 616-618.
[23]
Kenny, P.; Leitao, A.; Montanari, C. Ligand efficiency metrics considered harmful. J. Comput. Aided Mol. Des., 2014, 28(7), 699-710.
[24]
Matta, C.; Massa, L.; Gubskaya, A.; Knoll, E. Can one take the logarithm or the sine of a dimensioned quantity or a unit? Dimensional analysis involving transcendental functions. J. Chem. Educ., 2011, 88(1), 67-70.
[25]
Zhou, H.; Gilson, M. Theory of free energy and entropy in noncovalent binding. Chem. Rev., 2009, 109(9), 4092-4107.
[26]
Nissink, J. Simple size-independent measure of ligand efficiency. J. Chem. Inf. Model., 2009, 49(6), 1617-1622.
[27]
Scott, J.; Waring, M. Practical application of ligand efficiency metrics in lead optimisation. Bioorg. Med. Chem., 2018, 26(11), 3006-3015.
[28]
Hann, M. Molecular obesity, potency and other addictions in drug discovery. MedChemComm, 2011, 2(5), 349-355.
[29]
Shultz, M.D. Two decades under the influence of the rule of five and the changing properties of approved oral drugs. J. Med. Chem., 2019, 62(4), 1701-1714.
[30]
Williams, G.; Ferenczy, G.; Ulander, J.; Keseru, G. Binding thermodynamics discriminates fragments from druglike compounds: A thermodynamic description of fragment-based drug discovery. Drug Discov. Today, 2017, 22(4), 681-689.
[31]
Reynolds, C.H.; Reynolds, R.C. Group additivity in ligand binding affinity: an alternative approach to ligand efficiency. J. Chem. Inf. Model., 2017, 57, 3086-3093.
[32]
Polanski, J.; Pedrys, A.; Duszkiewicz, R.; Gasteiger, J. Scoring
ligand efficiency: Potency, ligand efficiency and product ligand efficiency
within big data landscape. Lett. Drug Des. Discov., 2017,
in print.
[33]
Polanski, J.; Duszkiewicz, R.; Pedrys, U.; Gasteiger, J. Scoring Ligand Efficiency. Acta Pol. Pharm., 2019, 76(4), 761-768.
Related Journals
Protein & Peptide Letters
Related Books
Frontiers in Protein and Peptide Sciences
Article Metrics
25
2
1
1