Skip to main content

Design of Virtual Combinatorial Libraries

  • Protocol

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 201))

Abstract

Last year marked the completion of the sequencing effort on the human genome. Advances in the fields of genomics and bioinformatics are widely expected to bring forth a large number of new biochemical targets for drug design by linking specific diseases to single genes or collections thereof. The pharmaceutical and biotechnology sector is now likely to become one of the most active industrial fields in the new century. A dramatic increase in the number of pharmaceutical targets in the near future would create a bottleneck of sorts at the stage of pharmaceutical drug discovery. Historically, this field has used a serial process, screening and optimizing compounds one at a time. However, the advent of combinatorial chemistry and high-throughput screening has significantly altered the face of drug discovery. Rapid generation and screening of combinatorial libraries has become commonplace in both industry and academia; with the ever-growing offering of organic reagents along with the vast palette of organic reactions, the chemical space accessible to combinatorial chemists has dramatically expanded. The application of combinatorial technology, however, is not without a potential caveat—even as the cost of synthesis and testing of a single chemical entity has fallen, it skyrockets when multiplied by the thousands or millions of combinatorial library members. Some of the potential solutions include (i) use of the three-dimensional target structure information in the design process; (ii) application of pharmacophore and quantitative structure-activity relationship (QSAR) methods to focus on libraries that most closely resemble screening hits; and (iii) reduction in the scale on which both medicinal chemistry and screening are currently performed. These solutions imply a need for a rational approach to reducing library size, custom izing the library for a given target, and more efficient in silico screening of a library (or, potentially, multiple libraries) of compounds against a large number of targets of interest. The challenge in accomplishing these goals is in being able to reduce the library while increasing the potential hit rate of individual library members.

This is a preview of subscription content, log in via an institution.

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Martin E. and Wong A. (2000) Sensitivity analysis and other improvements to tailored combinatorial library design. J. Chem. Inf. Comput. Sci. 40, 215–220.

    Article  PubMed  CAS  Google Scholar 

  2. Gorse D. and Lahana R. (2000) Functional diversity of compound libraries. Current Opinion in Chemical Biology 4, 287–294

    Article  PubMed  CAS  Google Scholar 

  3. Martin E. J. and Critchlow R. E. (1999) Beyond mere diversity: tailoring combinatorial libraries for drug discovery. J. Comb. Chem. 1, 32–45.

    Article  PubMed  CAS  Google Scholar 

  4. 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.

    Article  CAS  Google Scholar 

  5. Ghose A. K., Viswanadhan V. N., and Wendoloski J. J. (1999) A knowledgebased approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem. 1, 55–68.

    Article  PubMed  CAS  Google Scholar 

  6. Bemis G. W. and Murcko M. A. (1996) The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893.

    Article  PubMed  CAS  Google Scholar 

  7. Fejzo J., Lepre C. A., Peng J. W., et al. (1999) The SHAPES strategy: an NMRbased approach for lead generation in drug discovery. Chem. Biol. 6, 755–769.

    Article  PubMed  CAS  Google Scholar 

  8. MacGregor M. J. and Muskal S. M. (2000) Pharmacophore fingerprinting. 2. Application to primary library design. J. Chem. Inf. Comput. Sci. 40, 117–125.

    Article  Google Scholar 

  9. Ajay, Walters W. P. and Murcko M. A. (1998) Can we learn to distinguish between “drug-like” and “nondrug-like” molecules? J. Med. Chem. 41, 3314–3324.

    Article  PubMed  CAS  Google Scholar 

  10. Pearlman R. S. and Smith K. M. (1999) Metric validation and the receptorrelevant subspace concept. J. Chem. Inf. Comput. Sci. 39, 28–35.

    Article  CAS  Google Scholar 

  11. Boehm H.-J. and Stahl M. (2000) Structure-based library design: molecular modeling merges with combinatorial chemistry. Current Opinion in Chemical Biology 4, 283–286.

    Article  Google Scholar 

  12. Ewing T. J. A. and Kuntz I. D. (1997) Critical evaluation of search algorithms for automated molecular docking and database screening. J. Comput. Chem. 18, 1175–1189.

    Article  CAS  Google Scholar 

  13. Goodsell D. S., Morris G. M., and Olson A. J. (1996) Automated docking of flexible ligands: application of AUTODOCK. J. Mol. Recognit. 9, 1–5.

    Article  PubMed  CAS  Google Scholar 

  14. Rarey M., Wefing S., and Lengauer T. (1996) Placement of medium-sized molecular fragments into active sites of proteins. J. Comput.-Aided Mol. Des. 10, 41–54.

    Article  PubMed  CAS  Google Scholar 

  15. McMartin C. and Bohacek R. S. (1997) QXP: powerful, rapid computer algorithms for structure-based drug design. J. Comput.-Aided Mol. Des. 11, 333–344.

    Article  PubMed  CAS  Google Scholar 

  16. Boehm H.-J. (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J. Comput.-Aided Mol. Des. 6, 61–78.

    Article  CAS  Google Scholar 

  17. Wang R., Liu L., Lai L., and Tang Y. (1998) SCORE: a new empirical method for estimating the binding affinity of a protein-ligand complex. J. Mol. Model. 4, 379–394.

    Article  CAS  Google Scholar 

  18. Eldridge M. D., Murray C. W., Auton T. R., Paolini G. V., and Mee R. P. (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput.-Aided Mol. Des. 11, 425–445.

    Article  PubMed  CAS  Google Scholar 

  19. Gehlhaar D. K., Verkhivker G. M., Rejto P. A., Sherman C. J., Fogel D. B., and Freer S. T. (1995) Molecular recognition of the inhibitor AG-1343 by HIV-1 protease—Conformationally flexible docking by evolutionary programming. Chem. Biol. 2, 317–324.

    Article  PubMed  CAS  Google Scholar 

  20. Halgren T. A. (1996) Merck molecular force field. I. Basis, form, scope, parametrization, and performance of MMFF94. J. Comput. Chem. 17, 490–519.

    Article  CAS  Google Scholar 

  21. Miller M. D., Kearsley S. K., Underwood D. J., and Sheridan R. P. (1994) FLOG—A system to select quasi-flexible ligands complementary to a receptor of known three-dimensional structure. J. Comput.-Aided Mol. Des. 8, 153–174.

    Article  PubMed  CAS  Google Scholar 

  22. Charifson P. S., Corkery J. J., Murcko M. A., and Walters W. P. (1999) Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem. 42, 5100–5109.

    Article  PubMed  CAS  Google Scholar 

  23. Pearlman D. A. and Charifson P. S. (2001) Improved scoring of ligand-protein interaction using OWFEG free energy grids. J. Med. Chem. 44, 502–511.

    Article  PubMed  CAS  Google Scholar 

  24. 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.

    Article  PubMed  CAS  Google Scholar 

  25. Stanton R. V., Mount J., and Miller J. L. (2000) Combinatorial library design: maximizing model-fitting compound within matrix synthesis constraints. J. Chem. Inf. Comput. Sci. 40, 701–705.

    Article  PubMed  CAS  Google Scholar 

  26. Knegtel R. M. A., Kuntz I. D., and Oshiro C. M. (1997) Molecular docking to ensembles of protein structures. J. Mol. Biol. 266, 424–440.

    Article  PubMed  CAS  Google Scholar 

  27. Kauvar L M., Villar H. O., Sportsman J. R., Higgins D. L., and Schmidt D. E. Jr. (1998) Protein affinity map of chemical space. J. Chromatogr. B 715, 93–102.

    Article  CAS  Google Scholar 

  28. Briem H. and Kuntz I. D. (1996) Molecular similarity based on DOCK-generated fingerprints. J. Med. Chem. 39, 3401–3408.

    Article  PubMed  CAS  Google Scholar 

  29. Andrews K. H. 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.

    Article  PubMed  CAS  Google Scholar 

  30. Weber L. (2000) High-diversity combinatorial libraries. Current Opinion in Chemical Biology 4, 295–302.

    Article  PubMed  CAS  Google Scholar 

  31. Kauvar L M., Higgins D. L., Villar H. O., et al. (1995) Predicting ligand binding to proteins by affinity fingerprinting. Chem. Biol. 2, 107–118.

    Article  PubMed  CAS  Google Scholar 

  32. Lamb M. L., Burdick K. W., Toba S., et al. (2001) Design, docking, and evaluation of multiple libraries against multiple targets. PROTEINS: structure, function, and genetics 42, 296–318.

    Article  CAS  Google Scholar 

  33. Aronov A. M., Munagala N. R., Kuntz I. D., and Wang C. C. (2001) Virtual screening of combinatorial libraries across a gene family. In search of inhibitors of Giardia lamblia guanine phosphoribosyltransferase. Antimicrob. Agents Chemother., 45, 2571–2576.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Humana Press Inc.

About this protocol

Cite this protocol

Aronov, A.M. (2002). Design of Virtual Combinatorial Libraries. In: English, L.B. (eds) Combinatorial Library. Methods in Molecular Biology™, vol 201. Springer, Totowa, NJ. https://doi.org/10.1385/1-59259-285-6:267

Download citation

  • DOI: https://doi.org/10.1385/1-59259-285-6:267

  • Publisher Name: Springer, Totowa, NJ

  • Print ISBN: 978-0-89603-980-3

  • Online ISBN: 978-1-59259-285-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics