Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The influence of lead discovery strategies on the properties of drug candidates

Key Points

  • For several years, the pharmaceutical industry has been suffering from a shortfall of approved new chemical entities. Differences in the physicochemical properties of recent leads and clinical candidates compared with those of historical leads and approved drugs are thought to have a major impact on compound-related attrition in clinical trials.

  • In this article, the physicochemical profiles of hits are compared with the profiles of the corresponding leads that have evolved from them via hit-to-lead optimization. The hit–lead pairs are classified by the hit discovery strategy such as high-throughput screening (HTS), fragment-based hit discovery, virtual screening and natural product based screening.

  • On average, HTS, fragment and natural product hits have similar ligand efficiency but HTS hits achieve this primarily via lipophilicity, whereas fragment and natural product hits achieve this via good complementarity and balanced properties. Therefore, fragment-based and natural product hits could be considered to be the preferred starting points over HTS hits.

  • Our data suggest that it is highly challenging to progress HTS hits while maintaining or reducing logP, and that non-HTS hits become lipophilic leads, indicating that most of the gain in potency during hit-to-lead optimization is reached via extending the molecules with hydrophobic moieties.

  • Fragment-derived leads seem similar to those derived from HTS, suggesting that the hit-to-lead optimization process has a major role in the poor physicochemical properties of candidates.

  • The statistical data suggest that the benefits of using HTS alternatives for hit discovery do not include improved leads or more efficient leads, but only novel starting points by accessing uncharted chemical space.

  • We propose that a paradigm shift towards giving more resources to interdisciplinary hit-to-lead optimization teams may bring about a more productive hit evolution process starting from hit discovery to the clinic.


Despite the widespread acceptance of guidelines related to desirable physicochemical properties of potential small-molecule drugs, key properties — such as lipophilicity — of recently developed clinical candidates and advanced lead compounds have been shown to differ significantly from those of historical leads and drugs. By analysing the physicochemical properties of a large database of hits and corresponding leads identified in the past decade, we show that this undesirable phenomenon can be traced back to the nature of high-throughput screening hits and hit-to-lead optimization practices. Conceptual and organizational adjustments may be required to enable a smooth lead-evolution process that reduces the chance of high compound-related attrition in clinical trials.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Averaged properties of hits and leads.
Figure 2: Lead classes (bottom) originated and developed from screening hits (top) against β-site amyloid precursor protein cleaving enzyme 1 (BACE1).
Figure 3: Distribution of hits and leads developed against β-site amyloid precursor protein cleaving enzyme 1 (BACE1) in property space defined by logP and ligand efficiency (LE).
Figure 4: LELP of hits and leads.
Figure 5: Distribution of ligand efficiency (LE) versus number of heavy atoms (Nheavy) for the different hit and lead classes.
Figure 6: Distribution of average hits and leads classified by the different lead identification methods.


  1. Carney, S. How can we avoid the productivity gap? Drug Discov. Today 10, 1011–1013 (2005).

    Article  PubMed  Google Scholar 

  2. Goodnow, R. A. & Gillespie, P. in Progress in Medicinal Chemistry Vol. 45 Ch. 1 (eds King, F. D. & Lawton, G.) 1–61 (Elsevier, Amsterdam, 2006).

    Google Scholar 

  3. Keseru, G. M. & Makara, G. M. Hit discovery and hit-to-lead approaches. Drug Discov. Today 11, 741–748 (2006).

    Article  PubMed  Google Scholar 

  4. Davis, A. M., Keeling, D. J., Steele, J., Tomkinson, N. P. & Tinker A. C. Components of successful lead generation. Curr. Top. Med. Chem. 5, 421–439 (2005).

    CAS  Article  PubMed  Google Scholar 

  5. Deprez, B. & Deprez-Poulain R. Trends in hit-to-lead: an update. Front. Med. Chem. 3, 653–673 (2006).

    Google Scholar 

  6. CMR International. Drug Discovery Performance Metrics Programme. CMR International web site [online], (2004).

  7. Edwards, R. A., Zhang, K. & Firth, L. Benchmarking chemistry functions within pharmaceutical drug discovery and preclinical development. Drug Discov. World 67–71 (2002).

  8. CMR International. Drug Discovery Performance Metrics Programme, CMR International. CMR International web site [online], (2005).

  9. Proudfoot, J. R. Drugs, leads, and drug-likeness: an analysis of some recently launched drugs. Bioorg. Med. Chem. Lett. 12, 1647–1650 (2002).

    CAS  Article  PubMed  Google Scholar 

  10. 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). Based on the property analysis of a large set of initial and optimized leads, the authors demonstrated for the first time that less complex molecules are more likely to become hits, albeit weaker binders.

    CAS  Article  PubMed  Google Scholar 

  11. Hann, M. M. & Oprea, T. I. Pursuing the leadlikeness concept in pharmaceutical research. Curr. Opin. Chem. Biol. 8, 255–263 (2004).

    CAS  Article  PubMed  Google Scholar 

  12. Lipinski, C. A. et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997).

    CAS  Article  Google Scholar 

  13. 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).

    CAS  Article  PubMed  Google Scholar 

  14. 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). This paper reports that current medicinal chemistry produces compounds with molecular mass and cLogP higher than that of oral drugs and development compounds, emphasizing the importance of lipophilicity.

    CAS  Article  Google Scholar 

  15. Fox, S. et al. High throughput screening: update on practices and success. J. Biomol. Screen. 11, 864–869 (2006).

    Article  PubMed  Google Scholar 

  16. Fox, S., Farr-Jones, S., Sopchak, L., Boggs, A. & Comley, J. High throughput screening: searching for higher productivity. J. Biomol. Screen. 9, 354–358 (2004).

    CAS  Article  PubMed  Google Scholar 

  17. Parker, C. N. & Bajorath, J. Towards unified compound screening strategies: a critical evaluation of error sources in experimental and virtual high-throughput screening. QSAR Comb. Sci. 25, 1153–1161 (2006).

    CAS  Article  Google Scholar 

  18. Macarron, R. Critical review of the role of HTS in drug discovery. Drug Discov. Today 11, 277–279 (2006). An analysis of transformations in HTS practices throughout the years with respect to targets, compounds and screening platforms.

    Article  PubMed  Google Scholar 

  19. Harper, G., Pickett, S. D. & Green, D. V. S. Design of a compound screening collection for use in high throughput screening. Comb. Chem. High Throughput Screen. 7, 63–70 (2004).

    CAS  Article  PubMed  Google Scholar 

  20. Fox, S., Farr-Jones, S. & Yund, M. A. High throughput screening for drug discovery: continually transitioning into new technology. J. Biomol. Screen. 4, 183–186 (1999).

    CAS  Article  PubMed  Google Scholar 

  21. Inglese, J. et al. Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries. Proc. Natl Acad. Sci. USA 31, 11473–11478 (2006).

    Article  Google Scholar 

  22. Crisman, T. J. et al. “Plate Cherry Picking”: a novel semi-sequential screening paradigm for cheaper, faster, information-rich compound selection. J. Biomol. Screen. 12, 320–327 (2007).

    CAS  Article  PubMed  Google Scholar 

  23. Di, L. & Kerns, E. H. Biological assay challenges from compound solubility: strategies for bioassay optimization. Drug Discov. Today 11, 447–451 (2006).

    Article  Google Scholar 

  24. Andrews, P. R., Craik D. J. & Martin, J. L. Functional group contributions to drug–receptor interactions. J. Med. Chem. 27, 1648–1657 (1984).

    CAS  Article  PubMed  Google Scholar 

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

    CAS  Article  PubMed  Google Scholar 

  26. 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  PubMed  Google Scholar 

  27. Hopkins, A. L., Groom, C. R. & Alex, A. Ligand efficiency: a useful metric for lead selection. Drug Discov. Today 9, 430–431 (2004). An introduction to the principle of ligand efficiency as a measure of the binding energy per non-hydrogen atom — a useful parameter in the selection and optimization of leads.

    Article  PubMed  Google Scholar 

  28. Makara, G. M. & Athanasopoulos, J. Improving success rates for lead generation using affinity binding technologies. Curr. Opin. Biotechnol. 16, 666–673 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Golebiowski, A., Klopfenstein, S. R. & Portlockz D. E. Lead compounds discovered from libraries: part 2. Curr. Opin. Chem. Biol. 7, 308–325 (2003).

    CAS  Article  PubMed  Google Scholar 

  30. Rees, D. C., Congreve, M., Murray, C. W. & Carr, R. Fragment-based lead discovery. Nature Rev. Drug Discov. 3, 660–672 (2004).

    CAS  Article  Google Scholar 

  31. Alex, A. A. & Flocco, M. M. Fragment-based drug discovery: what has it achieved so far? Curr. Top. Med. Chem. 7, 1544–1567 (2007).

    CAS  Article  PubMed  Google Scholar 

  32. Harvey, A. L. Natural products as a screening resource. Curr. Opin. Chem. Biol. 11, 480–484 (2007).

    CAS  Article  PubMed  Google Scholar 

  33. McInnes, C. Virtual screening strategies in drug discovery. Curr. Opin. Chem. Biol. 11, 494–502 (2007).

    CAS  Article  PubMed  Google Scholar 

  34. Wyss, D. F., McCoy, M. A. & Senior, M. M. NMR-based approaches for lead discovery. Curr. Opin. Drug Discov. Dev. 5, 630–647 (2002).

    CAS  Google Scholar 

  35. Lipinski, C. A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 235–249 (2000).

    CAS  Article  PubMed  Google Scholar 

  36. 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).

    CAS  Article  PubMed  Google Scholar 

  37. Oprea, T. I. et al. Lead-like, drug-like or “pub-like”: how different are they? J. Comp. Aided Mol. Des. 21, 113–119 (2007).

    CAS  Article  Google Scholar 

  38. Fobare, W. F. et al. Thiophene substituted acylguanidines as BACE1 inhibitors. Bioorg. Med. Chem. Lett. 17, 5353–5356 (2007).

    CAS  Article  PubMed  Google Scholar 

  39. Coburn, C. A. et al. Identification of a small molecule nonpeptide active site β-secretase inhibitor that displays a nontraditional binding mode for aspartyl proteases. J. Med. Chem. 47, 6117–6119 (2004).

    CAS  Article  PubMed  Google Scholar 

  40. Durham T. B. & Shepherd, T. A. Progress toward the discovery and development of efficacious BACE inhibitors. Curr. Opin. Drug Discov. Dev. 9, 776–791 (2006).

    CAS  Google Scholar 

  41. Congreve, M. et al. Application of fragment screening by X-ray crystallography to the discovery of aminopyridines as inhibitors of β-secretase. J. Med. Chem. 50, 1124–1132 (2007).

    CAS  Article  PubMed  Google Scholar 

  42. Edwards, P. D. et al. Application of fragment-based lead generation to the discovery of novel, cyclic amidine β-secretase inhibitors with nanomolar potency, cellular activity, and high ligand efficiency. J. Med. Chem. 50, 5912–5925 (2007).

    CAS  Article  PubMed  Google Scholar 

  43. Kuglstatter, A. et al. Tyramine fragment binding to BACE-1 Bioorg. Med. Chem. Lett. 18, 1304–1307 (2008).

    CAS  Article  PubMed  Google Scholar 

  44. Reynolds, C. H., Tounge, B. A. & Bembenek, S. D. Ligand binding efficiency: trends, physical basis, and implications J. Med. Chem. 51, 2432–2438 (2008).

    CAS  Article  PubMed  Google Scholar 

  45. Congreve, M., Chessari, G., Tisi, D. & Woodhead, A. J. Recent developments in fragment-based drug discovery. J. Med. Chem. 51, 3661–3680 (2008). A recent review that highlights the trends in fragment screening, screening libraries and fragment optimization strategies.

    CAS  Article  PubMed  Google Scholar 

Download references


The authors are grateful to R. Kiss and L. Molnár for their help with compiling the hit-to-lead database, and to M. Hann (GlaxoSmithKline) and T. Oprea (University of New Mexico, USA) for kindly providing their lead-to-drug databases for comparison.

Author information

Authors and Affiliations


Corresponding author

Correspondence to György M. Keserü.

Supplementary information

Supplementary information S1 (box)

The influence of lead discovery strategies on the properties of drug candidates (PDF 684 kb)

Supplementary information S2 (figure)

(PDF 727 kb)

Supplementary information S3 (figure)

(PDF 801 kb)

Supplementary information S4 (figure)

(PDF 158 kb)

Supplementary information S5 (figure)

(PDF 178 kb)



A hit cluster includes molecules that are related by either visual inspection or a computed similarity measure.


A small-molecule hit that is unique (that is, a cluster of one) by either visual inspection or a computed similarity measure.


Similar to drugs. A term typically used for small molecules that resemble oral drugs (that is, do not have undesirable moieties) and have properties that do not violate Lipinski's rules.


pPotency is −log(potency), in which potency is either a measure of binding (Kd) or biological activity (Ki, IC50 or EC50).

Andrews' binding energy

An estimated binding energy a small molecule would establish with a hypothetical macromolecule partner if all functional groups contributed to binding. Average functional group contributions to binding were calculated by Andrews (applying a training set of 200 drugs) using a formula that includes correction for entropy.

Heavy atom

An atom other than hydrogen in small molecules that are relevant for drug discovery.

Polar surface area

Usually defined as the surface sum over all polar atoms (usually oxygen and nitrogen), which also includes attached hydrogens.


Rotatable bonds in molecules: acyclic single bonds that do not have significantly restricted rotational freedom (that is, a secondary amide bond is not a rotatable bond). Different software packages have various levels of sophistication in identifying rotatable bonds, and often the number of rotors simply equals the number of acyclic single bonds.

Veber's criteria

GlaxoSmithKline's database of 1,100 molecules with oral bioavailability measurements in rats was used by Veber et al. to find that a polar surface area of ≤140 Å2 (or the sum of hydrogen-bond donors and acceptors of ≤12) and a number of rotatable bonds of ≤10 is sufficient to predict oral bioavailability.

Lipinski's rules

Four criteria identified by Lipinski et al. to be relevant for oral bioavailability: molecular mass <500 Da, number of hydrogen-bond acceptors <10, number of hydrogen-bond donors <5 and clogP <5.


Similar to leads. A term typically used for small molecules that resemble leads that could historically be developed into oral drugs. Actual criteria vary but typically lead-like molecules do not have undesirable moieties and have properties that do not violate a stricter version of Lipinski's rules such as molecular mass <350 Da and logP <3.


Criteria proposed to describe fragments suitable for biophysical screening: molecular mass <300 Da, logP ≤3, the number of hydrogen-bond acceptors ≤3 and the number of hydrogen-bond donors ≤3.

Ligand efficiency

A measure proposed to quantify the average contribution of a heavy atom to binding (Gibb's free energy divided by the number of heavy atoms). Originally intended to be used with Kd but since has often been applied to any measure of potency.

Tanimoto distance

Molecular similarity as measured by the Tanimoto coefficient: T = c/(a + b + c), where c is the number of common bits, and a and b are the number of unique bits in molecule_a and molecule_b using a molecular fingerprint. The more similar two molecules are, the closer the Tanimoto distance is to 1. Typically, a Tanimoto coefficient >0.85 is considered highly similar and coefficient >0.75 is considered similar for the purpose of clustering molecules that may have similar biological activity profiles.

Hit-to-lead programmes

Viable leads delivered by successful hit-to-lead programmes fulfil a set of programme-dependent predefined criteria that typically include, but are not limited to, potency, selectivity, structure–activity relationships, physicochemical properties, metabolic stability, pharmacokinetics, and cytochrome P450 and hERG profiles.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Keserü, G., Makara, G. The influence of lead discovery strategies on the properties of drug candidates. Nat Rev Drug Discov 8, 203–212 (2009).

Download citation

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing