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Beyond mere diversity: tailoring combinatorial libraries for drug discovery.

E J Martin1, R E Critchlow

  • 1Chiron Corporation, Emeryville, California 94608, USA.

Journal of Combinatorial Chemistry
|April 4, 2000
PubMed
Summary

Optimizing combinatorial library design involves balancing diversity with crucial properties like polarity and cost. This approach enhances the discovery of drug leads by creating well-tailored chemical libraries through advanced statistical methods.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Combinatorial library design aims to maximize the discovery of useful compounds, such as drug leads.
  • Initial strategies focused on diversity, with limited consideration for other critical factors.
  • Effective library design requires balancing diversity with desirable compound properties.

Purpose of the Study:

  • To develop a method for "tailoring" combinatorial libraries by categorizing substituents based on properties like polarity, pharmacophoric features, rigidity, molecular weight, and cost.
  • To utilize D-optimal design for generating diverse library designs that align with desired property profiles.
  • To compare diversity scores across different design profiles to understand trade-offs between diversity, physical properties, synthetic difficulty, expense, and pharmacophoric bias.

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Main Methods:

  • Assigning candidate substituents to categories (e.g., polar, pharmacophoric, rigid, low molecular weight, expensive).
  • Employing stratified sampling via successive steps of D-optimal design.
  • Calibrating diversity scores by evaluating optimal designs from subsets of candidates or randomly eliminated candidates.

Main Results:

  • D-optimal design generates diverse libraries consistent with desirable property profiles.
  • Comparing diversity scores reveals trade-offs between diversity, physical properties, synthetic difficulty, expense, and pharmacophoric bias.
  • The procedure demonstrates how biased optimal designs outperform poor random designs.

Conclusions:

  • Tailored combinatorial library design, integrating computational and synthetic expertise, is crucial for drug discovery.
  • Specialized interactive software facilitates the synergistic effort needed for effective, well-tailored library design.
  • Balancing diversity with property-based categorization leads to more successful drug lead identification.