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Comparison of Methods Based on Diversity and Similarity for Molecule Selection and the Analysis of Drug Discovery Data

  • Raymond L.H. Lam
  • William J. Welch
Protocol
Part of the Methods in Molecular Biology™ book series (MIMB, volume 275)

Abstract

The concepts of diversity and similarity of molecules are widely used in quantitative methods for designing (selecting) a representative set of molecules and for analyzing the relationship between chemical structure and biological activity. We review methods and algorithms for design of a diverse set of molecules in the chemical space using clustering, cell-based partitioning, or other distance-based approaches. Analogous cell-based and clustering methods are described for analyzing drug-discovery data to predict activity in virtual screening. Some performance comparisons are made. The choice of descriptor variables to characterize chemical structure is also included in the comparative study. We find that the diversity of a selected set is quite sensitive to both the statistical selection method and the choice of molecular descriptors and that, for the dataset used in this study, random selection works surprisingly well in providing a set of data for analysis.

Key Words

Biological activity cell-based partitioning chemical descriptors classification clustering distance-based design diversity selection high-throughput screening quantitative structure-activity relationship 

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Copyright information

© Humana Press Inc. 2004

Authors and Affiliations

  • Raymond L.H. Lam
    • 1
  • William J. Welch
    • 2
    • 3
  1. 1.Department of Data Exploration SciencesGlaxoSmithKlineKing of PrussiaUSA
  2. 2.Department of StatisticsUniversity of British ColumbiaVancouver
  3. 3.Department of Statistics and Actuarial ScienceUniversity of WaterlooWaterlooCanada

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