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Deconvolution of combinatorial libraries for drug discovery: a model system

S M Freier1, D A Konings, J R Wyatt

  • 1ISIS Pharmaceuticals, Carlsbad, California 92008.

Journal of Medicinal Chemistry
|January 20, 1995
PubMed
Summary

Iterative synthesis and screening methods can effectively identify highly active molecules from complex libraries. Computer simulations show these techniques generally find the best or near-best molecule, even with multiple active compounds.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry

Background:

  • Iterative synthesis and screening strategies offer advantages for identifying active molecules from large combinatorial libraries.
  • Traditional methods often require extensive analytical separations and structural determination.

Purpose of the Study:

  • To evaluate the effectiveness of iterative deconvolution methods in identifying the most active molecules from complex libraries.
  • To investigate the impact of multiple active compounds and unrandomization order on selection success.
  • To assess the influence of experimental errors on iterative screening outcomes.

Main Methods:

  • Development of a model system utilizing oligonucleotide hybridization.
  • Extensive computer simulations to analyze iterative deconvolution processes.

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  • Evaluation of factors including library composition and experimental error.

    • Main Results:

    • Iterative deconvolution methods generally identify the most active molecule or one with very similar activity.
    • Achievable experimental and library synthesis errors do not typically prevent selection of near-optimal molecules.
    • The presence of numerous active compounds affects subset activity profiles but not the overall success in finding optimal molecules.

    Conclusions:

    • Iterative synthesis and screening are robust methods for identifying potent molecules in complex chemical libraries.
    • The described simulation model provides insights into optimizing iterative deconvolution strategies.
    • These findings support the reliability of iterative methods in drug discovery and chemical library optimization.