Abstract
Drug-likeness is a frequently invoked, although not always precisely defined, concept in drug discovery. Opinions on drug-likeness are to a large extent shaped by the relationships that are observed between surrogate measures of drug-likeness (e.g. aqueous solubility; permeability; pharmacological promiscuity) and fundamental physicochemical properties (e.g. lipophilicity; molecular size). This article draws on examples from the literature to highlight approaches to data analysis that exaggerate trends in data and the term correlation inflation is introduced in the context of drug discovery. Averaging groups of data points prior to analysis is a common cause of correlation inflation and results from analysis of binned continuous data should always be treated with caution.
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References
Ziliak ST, McCloskey DN (2008) The cult of statistical significance: How the standard error costs us jobs, justice and lives. University of Michigan Press, Ann Arbor
Kelley K, Preacher KJ (2012) On effect size. Psychol Methods 17:137–152
Hajduk PJ, Huth JR, Fesik SW (2005) Druggability indices for protein targets derived from NMR-based screening data. J Med Chem 48:2518–2525
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25
Abraham MH, Chadha HS, Whiting GS, Mitchell RC (1994) Hydrogen bonding. 32. An analysis of water-octanol and water-alkane partitioning and the Δlog P parameter of Seiler. J Pharm Sci 83:1085–1100
Colclough N, Hunter A, Kenny PW, Kittlety RS, Lobedan L, Tam KY, Timms MA (2008) High throughput solubility determination with application to selection of compounds for fragment screening. Bioorg Med Chem 16:6611–6616
Smith DA, Di L, Kerns EH (2010) The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nat Rev Drug Discov 9:929–939
Ekins S, Honeycutt JD, Metz JT (2010) Multiobjective optimization for drug discovery. In: Abraham DJ, Rotella DP (eds) Burger’s medicinal chemistry, drug discovery and development, 7th edn. Wiley, New York
Hopkins AL, Groom CR, Alex A (2004) Ligand efficiency: a useful metric for lead selection. Drug Discov Today 9:430–431
van de Waterbeemd H, Smith DA, Jones BC (2001) Lipophilicity in PK design: methyl, ethyl, futile…. J Comput-Aided Mol Des 15:273–286
Hann MM, Leach AR, Harper G (2001) Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comp Sci 41:856–864
Woltosz WS (2012) If we designed airplanes like we design drugs. J Comput-Aided Mol Des 26:159–163
Kenny PW (2009) Hydrogen bonding, electrostatic potential and molecular design. J Chem Inf Model 49:1234–1244
Rodgers JL, Nicewander WA (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42:59–66
Hou TJ, Xia K, Zhang W, Xu XJ (2004) ADME evaluation in drug discovery. 4. prediction of aqueous solubility based on atom contribution approach. J Chem Inf Comp Sci 44:266–275
ADME/T prediction models and databases. http://modem.ucsd.edu/adme/databases/databases_logS.htm. Accessed 15 Oct 2012
LOGKOW, A databank of evaluated octanol-water partition coefficients. http://logkow.cisti.nrc.ca/logkow/index.jsp. Accessed 26 Oct 2012
OEChem Toolkit Manual, OpenEye Scientific Software, Santa Fe, NM 87508. http://www.eyesopen.com/docs/toolkits/current/html/OEChem_TK-c++/index.html. Accessed 26 Oct 2012
SMARTS Theory Manual, Daylight Chemical Information Systems, Inc., Laguna Niguel, CA 92677. http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html. Accessed 16 Dec 2012
JMP version 10.0.0, SAS Institute, Cary, NC 27513. http://www.jmp.com. Accessed 16 Dec 2012
Hopkins AL, Mason JS, Overington JP (2006) Can we rationally design promiscuous drugs? Curr Opin Struct Biol 16:127–136
Leeson PD, Springthorpe B (2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6:881–890
Lovering F, Bikker J, Humblet C (2009) Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 52:6752–6756
Maxwell JC (1874) Van der Waals on the continuity of gaseous and liquid states. Nature 10:477–480
Gleeson MP (2008) Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem 51:817–834
Tarcsay A, Kinga N, Keserű GM (2012) Impact of lipophilic efficiency on compound quality. J Med Chem 55:1252–1260
Ritchie TJ, Ertl P, Lewis R (2011) The graphical representation of ADME-related molecule properties for medicinal chemists. Drug Discov Today 16:65–72
Hill AP, Young RJ (2010) Getting physical in drug discovery: a contemporary perspective on solubility and hydrophobicity. Drug Discov Today 15:648–655
Ritchie TJ, MacDonald SJF (2009) The impact of aromatic ring count on compound developability: are too many aromatic rings a liability in drug design? Drug Discov Today 14:1011–1020
Kenny PW (2012) Computation, experiment and molecular design. J Comput-Aided Mol Des 26:69–72
Johnstone C (2012) Medicinal chemistry matters—a call for discipline in our discipline. Drug Discov Today 17:538–543
Stahl M, Bajorath J (2011) Computational medicinal chemistry. J Med Chem 54:1–2
Acknowledgments
We thank Anthony Nicholls for valuable advice and the reviewers of the manuscript for their helpful and constructive feedback. We are grateful to Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Conselho Nacional de Pesquisa (CNPq) for financial support and OpenEye Scientific Software for an academic software license.
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Kenny, P.W., Montanari, C.A. Inflation of correlation in the pursuit of drug-likeness. J Comput Aided Mol Des 27, 1–13 (2013). https://doi.org/10.1007/s10822-012-9631-5
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DOI: https://doi.org/10.1007/s10822-012-9631-5