Approaches to Library Design for Combinatorial Chemistry

  • Stefan Güssregen
  • Bernd Wendt
  • Mark Warne
Part of the Methods in Molecular Biology™ book series (MIMB, volume 201)


With the advent of high-throughput technologies in drug discovery, combinatorial chemistry has superseded natural products as the prime source of compounds to be tested in biological screening (1). The reason being that compared with classical synthesis, high-throughput chemistry can produce a much larger number of compounds within a much shorter period of time. However, owing to the combinatorial nature of the problem and the large number of reagents available, many more compounds are theoretically accessible via a given synthetic route than it is actually feasible to synthesize. Therefore, library design techniques are employed to identify those reagents that yield a library enriched with the desired properties. Early on, most applications of these techniques concentrated on designing very diverse libraries, with the idea that these libraries would be tested against a variety of biological assays. The ideal library in this case would be with no voids, no redundancy, and an even distribution with regard to a given chemical space. Nowadays, it is equally common to design targeted libraries to have a maximal activity in a selected assay. Here, all compounds would be designed to be similar to given hits or leads. Other applications of library design techniques include the computational evaluation of templates, i.e., prioritization of candidate libraries for synthesis and screening (see Note 1).


Variation Site Command Line Combinatorial Chemistry Library Design Constant Substituent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Gibbon J. A., Taylor E. W., and Braeckman R. A. (1998) in Combinatorial chemistry and molecular diversity in drug discovery, (Gordon E. M. and Kerwin J. F., eds.), Wiley-Liss New York, NY, pp. 453–474.Google Scholar
  2. 2.
    Martin E. J. and Critchlow R. E. (1999) Beyond mere diversity: Tailoring combinatorial libraries for drug discovery. J. Combinat. Chem. 1, 32–45.CrossRefGoogle Scholar
  3. 3.
    Gorse D. and Lahana R. (2000) Functional diversity of compound libraries. Curr. Opin. Chem. Biol. 4, 287–294.PubMedCrossRefGoogle Scholar
  4. 4.
    Walters W.P, Stahl M. T., and Murcko M. A. (1998) Virtual screening—an overview. Drug Discov. Today 3, 160–178.CrossRefGoogle Scholar
  5. 5.
    Leach A. R. and Hann M. M. (2000) The in silico world of virtual libraries. Drug Discov. Today 5, 326–336.PubMedCrossRefGoogle Scholar
  6. 6.
    Good A. C. and Lewis R. A. (1997) New methodology for profiling combinatorial libraries and screening sets: cleaning up the design process with HARPick. J. Med. Chem. 40, 3926–3936.PubMedCrossRefGoogle Scholar
  7. 7.
    Van Drie J. H. and Lajiness M. S. (1998) Approaches to virtual library design. Drug Discov. Today 3, 274–283.CrossRefGoogle Scholar
  8. 8.
    Linusson A., Gottfries J., Lindgren F., and Wold S. (2000) Statistical molecular design of building blocks for combinatorial chemistry. J. Med. Chem. 43,1320–1328.PubMedCrossRefGoogle Scholar
  9. 9.
    Gillet V. J., Willet P., and Bradshaw J. (1997) The effectiveness of reactant pools for generation of structurally diverse combinatorial libraries. J. Chem. Inf. Comput. Sci. 37, 731–740.CrossRefGoogle Scholar
  10. 10.
    Jamois E. A., Hassan M., and Waldman M. (2000) Evaluation of reagent-based and product-based strategies in the design of combinatorial library subsets. J. Chem. Inf. Comput. Sci. 40, 63–70.PubMedCrossRefGoogle Scholar
  11. 11.
    Gillet V. J., Willet P., Bradshaw J., and Green D. V. S. (1999) Selecting combinatorial libraries to optimize diversity and physical properties. J. Chem. Inf. Comput. Sci. 39, 169–177.CrossRefGoogle Scholar
  12. 12.
    Pearlman R. S. and Smith K. M. (1998) Novel software tools for chemical diversity. Perspect. Drug Discov. Design. 9, 339–353.CrossRefGoogle Scholar
  13. 13.
    Pearlman R. S. and Smith K. M. (1999) Metric validation and the receptorrelevant subspace concept. J. Chem. Inf. Comput. Sci. 39, 28–35.CrossRefGoogle Scholar
  14. 14.
    Cramer R. D., Patterson D. E., Clark R. D., Soltanshahi F., and Lawless M. S. (1998) Virtual compound libraries: A new approach to decision making in molecular discovery research. J. Chem. Inf. Comput. Sci., 38, 1010–1023.CrossRefGoogle Scholar
  15. 15.
    Rishton G. M. (1997) Reactive compounds and in vitro false positives in HTS. Drug Discov. Today 2, 382–384.CrossRefGoogle Scholar
  16. 16.
    Lewis R. A., Mason J. S., and McLay I. M. (1997) Similarity measures for rational set selection and analysis of compbinatorial libraries: the diverse property-derived (DPD) approach. J. Chem. Inf. Comput. Sci. 37, 599–614.PubMedCrossRefGoogle Scholar
  17. 17.
    Hann M., Hudson B., Lewell X., Lifely R., Miller L., and Ramsden N. (1999)Strategic pooling of compounds for high-throughput screening. J. Chem. Inf. Comput. Sci. 39, 897–902.PubMedCrossRefGoogle Scholar
  18. 18.
    Clark D. E. and Pickett S. D. (2000) Computational methods for the prediction of “drug-likeness.” Drug Discov. Today 5, 49–58.PubMedCrossRefGoogle Scholar
  19. 19.
    Lipinski C. A., Lombardo F., Dominy B. W., and Feeney P. J. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 23, 3–25.CrossRefGoogle Scholar
  20. 20.
    Patterson D. E., Cramer R. D., Ferguson A. M., Clark R. D., and Weinberger L. E. (1996) Neighborhood behavior: A useful concept for validation of “molecular diversity” descriptors. J. Med. Chem. 39, 3049–3059.PubMedCrossRefGoogle Scholar
  21. 21.
    Johnson M. A. and Maggiora G. M. (1990) Concepts and applications of molecular similarity, Wiley New York.Google Scholar
  22. 22.
    Mason J. S., Morize I., Menard P. R., Cheney D. L., Hulme C., and Labaudiniere R. F. (1999) New 4-point pharmacophore method for molecular similarity and diversity applications: overview of the method and applications, including a novel approach to the design of combinatorial libraries containing priviledged substructures. J. Med. Chem. 42, 3251–3264.PubMedCrossRefGoogle Scholar
  23. 23.
    Picket S. D., Mason J. S., and McLay I. M. (1996) Diversity profiling and design using 3D pharmacophores: pharmacophore-derived queries (PDQ). J. Chem. Inf. Comput. Sci. 36, 1214–1223.CrossRefGoogle Scholar
  24. 24.
    Drewry D. H. and Young S. S. (1999) Approaches to the design of combinatorial libraries. Chemometr. Intell. Lab. Syst. 48, 1–20.CrossRefGoogle Scholar
  25. 25.
    Gorse D., Rees A., Kaczorek M., and Lahana R. (1999) Molecular diversity and its analysis. Drug Discov. Today 4, 257–264.PubMedCrossRefGoogle Scholar
  26. 26.
    Mason J. S. and Hermsmeier M. A. (1999) Diversity assessment. Curr. Opin. Chem. Biol. 3, 342–349.PubMedCrossRefGoogle Scholar
  27. 27.
    Brown R. D. and Martin Y. C. (1996) Use of structure-activity data to compare structure-based clustering methods and descriptors for use in compound selection. J. Chem. Inf. Comput. Sci. 36, 572–584CrossRefGoogle Scholar
  28. 28.
    Matter H. (1997) Selecting optimally diverse compounds from structure databases: a validation study of two-dimensional and three-dimensional molecular descriptors. J. Med. Chem. 40, 1219–1229.PubMedCrossRefGoogle Scholar
  29. 29.
    Cramer R. D., Clark R. D., Patterson D. E., and Ferguson A. M. (1996) Bioisosterism as a molecular diversity descriptor: steric fields of single topomeric conformers. J. Med. Chem. 39, 3060–3069.PubMedCrossRefGoogle Scholar
  30. 30.
    Andrews K. M. and Cramer R. D. (2000) Toward general methods of targeted library design: topomer shape similarity searching with diverse structures as queries. J. Med. Chem. 43, 1723–1740.PubMedCrossRefGoogle Scholar
  31. 31.
    Cramer R. D., Poss M. A., Hermsmeier M. A., Caulfield T. J., Kowala M. C., and Valentine M. T. (1999) Prospective identification of biologically active structures by topomer shape similarity searching. J. Med. Chem. 42, 3919–3933.PubMedCrossRefGoogle Scholar
  32. 32.
    Clark R. D., Patterson D. E., Soltanshahi F., Blake J. F., and Matthew J. B. (2000) Visualizing substructural fingerprints. J. Mol. Graphics Mod. 18, 404–411.CrossRefGoogle Scholar
  33. 33.
    Clark R. D. (1997) OptiSim: An extended dissimilarity selection method for finding diverse representative subsets. J. Chem. Inf. Comput. Sci. 37, 1181–1188.CrossRefGoogle Scholar
  34. 35.
    Ash S., Cline M. A., Homer R. W., Hurst T., and Smith G. B. (1997) SYBYL line notation (SLN): a versatile language for chemical structure representation. J. Chem. Inf. Comput. Sci. 37, 71–79.CrossRefGoogle Scholar
  35. 36.
    Weininger D. J. (1988) SMILES, a chemical language and information system.1. Introduction of methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28,31–36.CrossRefGoogle Scholar
  36. 37.
    Agrafiotis K. and Lobanov V. S. (2000) Ultrafast algorithm for designing focused combinatorial arrays. J. Chem. Inf. Comput. Sci. 40, 1030–1038.PubMedCrossRefGoogle Scholar
  37. 38.
    Dalby A., Nourse J. G., Hounshell W. D., et al. (1992) Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited. J. Chem. Inf. Comput. Sci. 32, 244–255.CrossRefGoogle Scholar
  38. 39.
    Willett P. (1986) Similarity and clustering in chemical information systems,Research Studies Press Letchwork, U. K.Google Scholar
  39. 40.
    Shi S., Peng Z., Kostrowicki J., Paderes G., and Kuki A. (2000) Efficient combinatorial filtering for desired molecular properties of reaction products. J. Mol.Graphics. Mod. 18, 478–496.CrossRefGoogle Scholar
  40. 41.
    Holliday J. D. and Willett P. (1996) Definitions of “dissimilarity” for dissimilarity-based compound selection. J. Biomol. Screening 1, 145–151.CrossRefGoogle Scholar
  41. 42.
    Willett P. and Winterman V. (1986) A comparison of some measures for the determination of intermolecular structural similarity. Quant. Struct. Activ. Relat. 5, 18–25.CrossRefGoogle Scholar
  42. 43.
    Kubinyi H. (ed.) (1993) 3D QSAR in drug design, ESCOM, Leiden The Netherlands.Google Scholar

Copyright information

© Humana Press Inc. 2002

Authors and Affiliations

  • Stefan Güssregen
    • 1
  • Bernd Wendt
    • 1
  • Mark Warne
    • 1
  1. 1.Tripos Receptor Research Ltd.UK

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