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Related Experiment Videos

Bioactive diversity and screening library selection via affinity fingerprinting

S L Dixon1, H O Villar

  • 1Telik, Inc., South San Francisco, California 94080, USA. sdixon@telik.com

Journal of Chemical Information and Computer Sciences
|December 10, 1998
PubMed
Summary

Affinity fingerprints, which describe compound binding to diverse proteins, overcome limitations of structure-based drug design. This method enables efficient selection of active compounds and diverse training sets for drug discovery.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • The Similarity Principle guides library design but struggles with structure-activity relationships.
  • Structurally similar compounds can have different biological activities.
  • Some targets bind diverse molecular structures.

Purpose of the Study:

  • To introduce affinity fingerprints as a superior alternative to structure-based compound selection.
  • To demonstrate the utility of affinity fingerprints in creating effective training sets and identifying potent compounds.

Main Methods:

  • Compounds are characterized by their binding affinity to a panel of functionally dissimilar proteins.
  • Affinity data is used to generate 'affinity fingerprints' for each compound.

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  • Simple algorithms are applied to affinity fingerprints for set selection and compound extraction.

    • Main Results:

    • Affinity fingerprints encode fundamental binding and activity factors, bypassing structural ambiguities.
    • The method successfully generates active-enriched diverse training sets.
    • Efficient extraction of highly active compounds from large libraries is achieved.

    Conclusions:

    • Affinity fingerprints offer a robust framework for overcoming limitations in traditional library design and compound selection.
    • This approach enhances the efficiency and effectiveness of drug discovery processes.
    • The method facilitates the identification of potent drug candidates through improved data representation.