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Computational methods in molecular diversity and combinatorial chemistry

M G Bures1, Y C Martin

  • 1Abbott Laboratories, Abbott Park, IL 60064-3500, USA. mark.bures@abbott.com

Current Opinion in Chemical Biology
|August 6, 1998
PubMed
Summary

Computational chemistry and molecular modeling advance drug discovery. New methods enhance molecular diversity and library design for medicinal chemistry applications.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • The field of medicinal chemistry is undergoing a transformation.
  • Advancements in molecular diversity, combinatorial chemistry, and automated synthesis are key drivers.
  • Computational chemistry and molecular modeling are essential tools to address current drug discovery challenges.

Purpose of the Study:

  • To highlight the role of computational chemistry and molecular modeling in modern drug discovery.
  • To discuss recent advancements in measuring molecular diversity.
  • To explore the application of genetic algorithms in designing diverse chemical libraries.

Main Methods:

  • Utilizing computational chemistry tools and molecular modeling techniques.

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  • Developing and applying novel measures for assessing molecular diversity.
  • Employing genetic algorithms for library design optimization.

    • Main Results:

    • Significant progress has been made in quantifying molecular diversity.
    • Genetic algorithms have proven effective in generating diverse molecular libraries.
    • These advancements support the creation of novel drug candidates.

    Conclusions:

    • Computational chemistry and molecular modeling are integral to contemporary drug discovery.
    • New metrics for molecular diversity and advanced library design strategies are crucial.
    • These innovations are paving the way for a new era in medicinal chemistry.