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Related Experiment Videos

OptDesign: extending optimizable k-dissimilarity selection to combinatorial library design.

Robert D Clark1, Julia Kar, Lakshmi Akella

  • 1Tripos, Inc., 1699 South Hanley Road, St. Louis, Missouri 63144, USA. bclark@tripos.com

Journal of Chemical Information and Computer Sciences
|May 28, 2003
PubMed
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Optimizable k-dissimilarity (OptiSim) selection was enhanced for interdependent variables in combinatorial sublibrary design. New methods ensure diverse and representative selections, efficiently generating complex designs.

Area of Science:

  • Computational Chemistry and Cheminformatics
  • Drug Discovery and Development
  • Materials Science

Background:

  • Optimizable k-dissimilarity (OptiSim) selection is a method for creating diverse and representative sets by sampling and selecting from subsamples.
  • The original OptiSim method was limited to single target populations and independent variables.

Purpose of the Study:

  • To extend the OptiSim methodology for vector selection of interdependent variables in combinatorial sublibrary design.
  • To introduce modifications enabling efficient generation of representative and diverse multiblock and sparse matrix designs.

Main Methods:

  • Introduced 'pivoting between variables' where subsamples are drawn from reagent pools sequentially, evaluating candidates based on isolation and product complementarity.
  • Implemented static (e.g., molecular weight) and dynamic (e.g., structural diversity) filters for candidate evaluation.

Related Experiment Videos

  • Added the ability to bias candidate selection for inclusion in subsamples, allowing for dual objectives (e.g., diversity and cost).

    • Main Results:

    • The modified OptiSim methodology efficiently generates representative and diverse combinatorial sublibraries, including multiblock and sparse matrix designs.
    • The approach simplifies 'backfilling' of designs when reagents or products are removed.
    • The method is intrinsically fast, avoiding full combinatorial enumeration and allowing for Pareto-optimal designs through biased selection.

    Conclusions:

    • The enhanced OptiSim method effectively addresses the challenges of selecting interdependent variables for combinatorial library design.
    • The modifications provide a flexible and efficient framework for generating high-quality, diverse, and representative chemical libraries.
    • The ability to incorporate biased selection offers a powerful tool for multi-objective optimization in design processes.