Ligand efficiency measures quantify the molecular properties, particularly size and lipophilicity, of small molecules that are required to gain binding affinity to a drug target. There are additional efficiency measures for groups in a molecule, and for combinations of size and lipophilicity.
The application of ligand efficiency metrics has been widely reported in the selection and optimization of fragments, hits and leads. In particular, optimization of lipophilic ligand efficiency shows that it is possible to increase affinity and reduce lipophilicity at the same time, even with challenging 'lipophile-preferring' targets.
Mean ligand efficiency measures of molecules acting at a specific target, when combined with their drug-like physicochemical properties, are a practical means of estimating target 'druggability'. This is exemplified with 480 target–assay pairs from the primary literature. Across these targets, correlations between biological activity in vitro and physicochemical properties are generally weak, which shows that increasing activity by increasing physicochemical properties is not always necessary.
An analysis of 46 recently marketed oral drugs shows that they frequently have highly optimized ligand efficiency values and lipophilic ligand efficiency values for their target. Compared with 'only-in-class' oral drugs, only 1.5% of all molecules per target — on average — possess superior combined ligand efficiency and lipophilic ligand efficiency values.
Optimizing ligand efficiencies based on both molecular size and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the molecular inflation that pervades current practice in medicinal chemistry, and to increase the ability to develop drug candidates.
The judicious application of ligand or binding efficiency metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimized ligand efficiency values for their targets. Optimizing ligand efficiency metrics based on both molecular mass and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the inflation of these properties that has been observed in current medicinal chemistry practice, and to increase the quality of drug candidates.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Novel tricyclic small molecule inhibitors of Nicotinamide N-methyltransferase for the treatment of metabolic disorders
Scientific Reports Open Access 14 September 2022
Subscribe to Journal
Get full journal access for 1 year
only $6.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Leeson, P. D. & Oprea, T. I. in Drug Design Strategies: Quantitative Approaches Ch. 2 (eds Livingstone, D. J. & Davis, A. M.) (Royal Society of Chemistry, 2012).
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).
Young, R. J., Green, D. V., Luscombe, C. N. & Hill, A. P. Getting physical in drug discovery II: the impact of chromatographic hydrophobicity measurements and aromaticity. Drug Discov. Today 16, 822–830 (2011).
Waring, M. Defining optimum lipophilicity and molecular weight ranges for drug candidates —molecular weight dependent lower logD limits based on permeability. Bioorg. Med. Chem. Lett. 19, 2844–2851 (2009).
Johnson, T. W. et al. Using the golden triangle to optimize clearance and oral absorption. Bioorg. Med. Chem. Lett. 19, 5560–5564 (2009).
Gleeson, M. P. Generation of a set of simple, interpretable ADMET rules of thumb. J. Med. Chem. 51, 817–834 (2008).
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). In this paper, LLE is proposed as a measure of specificity.
Hughes, J. D. et al. Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett. 18, 4872–4875 (2008).
Luker, T. et al. Strategies to improve in vivo toxicology outcomes for basic candidate drug molecules. Bioorg. Med. Chem. Lett. 21, 5673–5679 (2011).
Wenlock, M. C., 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).
Leeson, P. D. & Empfield, J. R. Reducing the risk of drug attrition associated with physicochemical properties. Ann. Rep. Med. Chem. 45, 393–407 (2010).
Leeson, P. D. & Davis, A. M. Time-related differences in the physical property profiles of oral drugs. J. Med. Chem. 47, 6338–6348 (2004).
Proudfoot, J. R. The evolution of synthetic oral drug properties. Bioorg. Med. Chem. Lett. 15, 1087–1090 (2005).
Leeson, P. D., St-Gallay, S. A. & Wenlock, M. C. Impact of ion class and time on oral drug molecular properties. Med. Chem. Commun. 2, 91–105 (2011).
Walters, W. P., Green, J., Weiss, J. R. & Murcko, M. A. What do medicinal chemists actually make? A 50-year retrospective. J. Med. Chem. 54, 6405–6416 (2011).
Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S. & Hopkins, A. L. Quantifying the chemical beauty of drugs. Nature Chem. 4, 90–98 (2012).
Leeson, P. D. & St-Gallay, S. A. The influence of the 'organizational factor' on compound quality in drug discovery. Nature Rev. Drug Discov. 10, 749–765 (2011).
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).
Blake, J. F. Examination of the computed molecular properties of compounds selected for clinical development. Biotechniques Suppl. 16–20 (2003).
Paolini, G. V., Shapland, R. H., van Hoorn, W. P., Mason, J. S. & Hopkins, A. L. Global mapping of pharmacological space. Nature Biotech. 24, 805–815 (2006).
Keserü, G. M. 5th Drug Design Lead Discovery Conference 2009: lead finding strategies and optimization case studies. Drugs Fut. 35, 143–153 (2010).
Hann, M. M. Molecular obesity, potency and other addictions in drug discovery. Med. Chem. Comm. 2, 349–355 (2011).
Keserü, G. M. & Makara, G. M. The influence of lead discovery strategies on the properties of drug candidates. Nature Rev. Drug Discov. 8, 203–212 (2009).
Oprea, T. I., Davis, A. M., Teague, S. J. & Leeson, P. D. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 41, 1308–1315 (2001).
Hann, M. M., Leach, A. R. & Harper, G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J. Chem. Inf. Comput. Sci. 41, 856–864 (2001). This paper explains why small compounds such as fragments have a higher probability than larger compounds (for example, those in a typical HTS library) of binding to protein targets.
Hopkins, A. L., Groom, C. R. & Alex, A. Ligand efficiency: a useful metric for lead selection. Drug Discov. Today 9, 430–431 (2004). This paper defines the LE concept and proposes it as a measure to help prioritize screening hits.
Kuntz, I. D., Chen, K., Sharp, K. A. & Kollman, P. A. The maximal affinity of ligands. Proc. Natl Acad. Sci. USA 96, 9997–10002 (1999). This seminal article lays the foundations for the derivation of LE metrics.
Andrews, P. R., Craik, D. J. & Martin, J. L. Functional group contributions to drug–receptor interactions. J. Med. Chem. 27, 1648–1657 (1984).
Shultz, M. D. Setting expectations in molecular optimizations: strengths and limitations of commonly used composite parameters. Bioorg. Med. Chem. Lett. 23, 5980–5991 (2013).
Freire, E. Do enthalpy and entropy distinguish first in class from best in class? Drug Discov. Today 13, 869–874 (2008).
Ferenczy, G. G., Keserü, G. M. in Physico-Chemical and Computational Approaches to Drug Discovery Ch. 2 (eds Luque, J. & Barril, X.) (Royal Society of Chemistry, 2012).
Olsson, T. S. G., Williams, M. A., Pitt, W. R. & Ladbury, J. E. The thermodynamics of protein–ligand interaction and solvation: insights for ligand design. J. Mol. Biol. 384, 1002–1017 (2008).
Freire, E. A thermodynamic approach to the affinity optimization of drug candidates. Chem. Biol. Drug Des. 74, 468–472 (2009).
Ferenczy, G. G. & Keserü, G. M. Thermodynamics guided lead discovery and optimization. Drug Discov. Today 15, 919–932 (2010).
Ladbury, J. E., Klebe, G. & Freire, E. Adding calorimetric data to decision making in lead discovery: a hot tip. Nature Rev. Drug Discov. 9, 23–27 (2010).
Ferenczy, G. G. & Keserü, G. M. Enthalpic efficiency of ligand binding. J. Chem. Inf. Mod. 50, 1536–1541 (2010).
Hann, M. M. & Keserü, G. M. Finding the sweet spot — the role of nature and nurture in medicinal chemistry. Nature Rev. Drug Discov. 11, 355–365 (2012).
Reynolds, C. H., Bembenek, S. D. & Tounge, B. A. The role of molecular size in ligand efficiency. Bioorg. Med. Chem. Lett. 17, 4258–4261 (2007).
Reynolds, C. H., Tounge, B. A. & Bembenek, S. D. Ligand binding efficiency: trends, physical basis, and implications. J. Med. Chem. 51, 2432–2438 (2008). This paper demonstrates that LE has a significant size-dependence that can be explained in terms of simple molecular principles.
Loving, K., Alberts, I. & Sherman, W. Computational approaches for fragment-based and de novo design. Curr. Top. Med. Chem. 10, 14–32 (2012).
Liu, T., Lin, Y., Wen, X., Jorissen, R. N. & Gilson, M. K. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucl. Ac. Res. 35, D198–D201 (2007).
Reynolds, C. H. & Holloway, M. K. Thermodynamics of ligand binding and efficiency. ACS Med. Chem. Lett. 2, 433–437 (2011).
Shultz, M. D. The thermodynamic basis for the use of lipophilic efficiency (LipE) in enthalpic optimizations, Bioorg. Med. Chem. Lett. 23, 5992–6000 (2013).
Tarcsay, A., Nyiri, K. & Keserü, G. M. Impact of lipophilic efficiency on compound quality. J. Med. Chem. 55, 1252–1260 (2012).
Hansch, C., Bjoerkroth, J. P. & Leo, A. Hydrophobicity and central nervous system agents: on the principle of minimal hydrophobicity in drug design. J. Pharm. Sci. 76, 663–687 (1987).
Waring, M. Lipophilicity in drug discovery. Exp. Opin. Drug Discov. 5, 235–248 (2010).
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 Deliv. Rev. 23, 3–25 (1997).
Mortenson, P. N. & Murray, C. W. Assessing the lipophilicity of fragments and early hits. J. Comput. Aided Mol. Des. 663–667 (2011).
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 Chem. Neurosci. 1, 420–434 (2010).
Perola, E. An analysis of the binding efficiencies of drugs and their leads in successful drug discovery programs. J. Med. Chem. 53, 2986–2997 (2010). This analysis of leads of 60 recently launched drugs shows that mean potency and LLE increased in optimization, whereas mean lipophilicity did not.
Morphy, R. The influence of target family and functional activity on the physicochemical properties of pre-clinical compounds. J. Med. Chem. 49, 2969–2978 (2006).
Baker, M. Fragment-based lead discovery grows up. Nature Rev. Drug Discov. 12, 5–7 (2013).
Tsai, J. et al. Discovery of a selective inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity. Proc. Natl Acad. Sci. USA 105, 3041–3046 (2008).
Verdonk, M. L. & Rees, D. C. Group efficiency: a guideline for hits-to-leads chemistry. ChemMedChem 3, 1179–1180 (2008).
Drysdale, M. J. & Brough, P. A. Medicinal chemistry of Hsp90 inhibitors. Curr. Top. Med. Chem. 8, 859–868 (2008).
Murray, C. W. et al. Fragment-based drug discovery applied to Hsp90. Discovery of two lead series with high ligand efficiency. J. Med. Chem. 53, 5942–5955 (2010).
Woodhead, A. J. et al. Discovery of (2,4-dihydroxy- 5-isopropylphenyl)-[5-(4-methylpiperazin-1-ylmethyl)-1,3-dihydrois oindol-2-yl]methanone (AT13387), a novel inhibitor of the molecular chaperone Hsp90 by fragment based drug design. J. Med. Chem. 53, 5956–5969 (2010).
Ferenczy, G. G. & Keserü, G. M. How are fragments optimized? A retrospective analysis of 145 fragment optimizations. J. Med. Chem. 56, 2478–2486 (2013).
Jhoti, H., Williams, G., Rees, D. C. & Murray, C. W. The 'rule of three' for fragment-based drug discovery: where are we now? Nature Rev. Drug Discov. 12, 644–645 (2013).
Vieth, M. & Sutherland, J. J. Dependence of molecular properties on proteomic family for marketed oral drugs. J. Med. Chem. 49, 3451–3453 (2006).
Kwong, A. D., Kauffman, R. S., Hurter, P. & Mueller, P. Discovery and development of telaprevir: an NS3-4A protease inhibitor for treating genotype 1 chronic hepatitis C virus. Nature Biotech. 29, 993–1003 (2011).
McTigue, M. et al. Molecular conformations, interactions, and properties associated with drug efficiency and clinical performance among VEGFR TK inhibitors. Proc. Natl Acad. Sci. USA 109, 18281–18289 (2012). This paper shows that LLE values of VEGFR inhibitors correlate with clinical efficacy.
Davis, M. I. et al. Comprehensive analysis of kinase inhibitor selectivity. Nature Biotech. 29, 1046–1051 (2011).
Soth, M. et al. 3-amido pyrrolopyrazine JAK kinase inhibitors: development of a JAK3 versus JAK1 selective inhibitor and evaluation in cellular and in vivo models. J. Med. Chem. 56, 345–356 (2013).
Young, R. J. The successful quest for oral factor Xa inhibitors; learnings for all of medicinal chemistry? Bioorg. Med. Chem. Lett. 21, 6228–6235 (2011). This article proposes a general approach for assessing compound quality, exemplified by clinically available factor Xa inhibitors, which have a lower hydrophobicity and higher LLE AT values than other published inhibitors.
Lemoine, R. C. & Wanner, J. Small molecule antagonists of the chemokine receptor CCR5. Curr. Top. Med. Chem. 10, 1299–1338 (2010).
Cumming, J. et al. Balancing hERG affinity and absorption in the discovery of AZD5672, an orally active CCR5 antagonist for the treatment of rheumatoid arthritis. Bioorg. Med. Chem. Lett. 22, 1655–1659 (2012).
Charles, M. A. & Kane, J. P. New molecular insights into CETP structure and function: a review. J. Lipid Res. 53, 1451–1458 (2012).
Mantlo, N. B. & Escribano, A. Update on the discovery and development of cholesteryl ester transfer protein inhibitors for reducing residual cardiovascular risk. J. Med. Chem. 57, 1–17 (2014).
Hunt, J. A. et al. 2-arylbenzoxazoles as CETP inhibitors: substitution and modification of the α-alkoxyamide moiety. Bioorg. Med. Chem. Lett. 20, 1019–1022 (2010).
Sweis, R. F. et al. 2-(4-carbonylphenyl)benzoxazole inhibitors of CETP: attenuation of hERG binding and improved HDLc-raising efficacy. Bioorg. Med. Chem. Lett. 21, 2597–2600 (2011).
Kallashi, F. et al. 2-arylbenzoxazoles as CETP inhibitors: raising HDL-C in cynoCETP transgenic mice. Bioorg. Med. Chem. Lett. 21, 558–561 (2011).
Harikrishnan, L. S. et al. 2-arylbenzoxazoles as novel cholesteryl ester transfer protein inhibitors: optimization via array synthesis. Bioorg. Med. Chem. Lett. 18, 2640–2644 (2008).
Fernanadez, M.-C. et al. Design, synthesis and structure-activity-relationship of 1,5-tetrahydronaphthyridines as CETP inhibitors. Bioorg. Med. Chem. Lett. 22, 3056–3062 (2012).
Harikrishnan, L. S. et al. Diphenylpyridylethanamine (DPPE) derivatives as cholesteryl ester transfer protein (CETP) inhibitors. J. Med. Chem. 55, 6162–6175 (2012).
Griffith, D. A. et al. Discovery of 1-[9-(4-chlorophenyl)-8-(2-chlorophenyl)-9H-purin-6-yl]-4 ethylaminopiperidine-4-carboxylic acid amide hydrochloride (CP-945,598), a novel, potent, and selective cannabinoid type 1 receptor antagonist. J. Med. Chem. 52, 234–237 (2009).
Plowright, A. T. et al. Discovery of agonists of cannabinoid receptor 1 with restricted central nervous system penetration aimed for treatment of gastroesophageal reflux disease. J. Med. Chem. 56, 220–240 (2013).
Darout, E. et al. Design and synthesis of diazatricyclodecane agonists of the G-protein-coupled receptor 119. J. Med. Chem. 56, 301–319 (2013).
Higueruelo, A. P., Schreyer, A., Bickerton, G. R. J., Blundell, T. L. & Pitt, W. R. What can we learn from the evolution of protein–ligand interactions to aid the design of new therapeutics? PLoS ONE 7, e51742 (2012).
Valko, K., Chiarparin, E., Nunhuck, S. & Montanari, D. In vitro measurement of drug efficiency index to aid early lead optimization. J. Pharm. Sci. 101, 4155–4169 (2012).
Freeman-Cook, K. D., Hoffman, R. L. & Johnson, T. W. Lipophilic efficiency: the most important efficiency metric in medicinal chemistry. Future Med. Chem. 5, 113–115 (2013).
Abad-Zapatero, C. Ligand efficiency indices for effective drug discovery. Exp. Opin. Drug Discov. 2, 469–488 (2007).
Mannhold, R., Poda, G. I., Ostermann, C. & Tetko, I. V. Calculation of molecular lipophilicity: state-of-the-art and comparison of logP methods on more than 96,000 compounds. J. Pharm. Sci. 98, 861–893 (2009).
Nissink, J. W. M. Simple size-independent measure of ligand efficiency. J. Chem. Inf. Model. 49, 1617–1622 (2009).
Southan, C., Boppana, K., Jagarlapudi, S. A. & Muresan, S. Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: ranking 1654 human protein targets by assayed compounds and molecular scaffolds. J. Cheminform. 3, 14 (2011).
Klibanov, O. M., Williams, S. H. & Iler, C. A. Cenicriviroc, an orally active CCR5 antagonist for the potential treatment of HIV infection. Curr. Opin. Investigat. Drugs 11, 940–950 (2010).
The authors thank N. Richmond (GlaxoSmithKline) for discussions on the derivation of efficiency metrics; G. Williams (Astex) for fruitful discussion on binding thermodynamics; R. Young (GlaxoSmithKline) for discussions on aromaticity and drug efficiency metrics; and AstraZeneca for providing access to the GVK BIO database.
The authors declare no competing financial interests.
Ligand efficiencies versus corresponding physical properties. (PDF 1260 kb)
Enthalpy and entropy ligand efficiencies versus properties. (PDF 2345 kb)
Mean, median and standard deviations of potencies, LE, LLE and LELP values for collections of oral drugs, Phase II compounds, hits and leads (PDF 361 kb)
- Spline fit
A statistical, numerical method for fitting a curve through a set of data points using a cubic polynomial.
The negative logarithm of activity in vitro in published papers: for example, half-maximal inhibitory concentration (IC50), inhibition constant (Ki) or effector concentration for half-maximum response (EC50) values.
About this article
Cite this article
Hopkins, A., Keserü, G., Leeson, P. et al. The role of ligand efficiency metrics in drug discovery. Nat Rev Drug Discov 13, 105–121 (2014). https://doi.org/10.1038/nrd4163
This article is cited by
Nature Reviews Drug Discovery (2022)
Nature Reviews Drug Discovery (2022)
Pharmacophore modeling, molecular docking, and molecular dynamics studies to identify new 5-HT2AR antagonists with the potential for design of new atypical antipsychotics
Molecular Diversity (2022)
Medicinal Chemistry Research (2022)
Journal of the Iranian Chemical Society (2022)