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Combination of molecular similarity measures using data fusion

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Perspectives in Drug Discovery and Design

Abstract

Many different measures of structural similarity have been suggested for matching chemical structures, each such measure focusing upon some particular type of molecular characteristic. The multi-faceted nature of biological activity suggests that an appropriate similarity measure should encompass many different types of characteristic, and this article discusses the use of data fusion methods to combine the results of searches based on multiple similarity measures. Experiments with several different types of dataset and activity suggest that data fusion provides a simple, but effective, approach to the combination of individual similarity measures. The best results were generally obtained with a fusion rule that sums the rank positions achieved by each molecule in searches using individual measures.

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Authors and Affiliations

  1. University of Sheffield, Western Bank, Sheffield, S10 2TN, U.K.

    Claire M.R. Ginn

  2. University of Sheffield, Western Bank, Sheffield, S10 2TN, U.K.

    Peter Willett

  3. GlaxoWellcome Research and Development Limited, Stevenage, SG1 2NY, U.K.

    John Bradshaw

Authors
  1. Claire M.R. Ginn
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  2. Peter Willett
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  3. John Bradshaw
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Correspondence to Peter Willett.

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Ginn, C.M., Willett, P. & Bradshaw, J. Combination of molecular similarity measures using data fusion. Perspectives in Drug Discovery and Design 20, 1–16 (2000). https://doi.org/10.1023/A:1008752200506

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