Chemical Data Mining Based on Structural Similarity

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  • TAKAHASHI Yoshimasa
    Dept. of Knowledge-based Information Engineering, Toyohashi University of Technology
  • FUJISHIMA Satoshi
    Dept. of Knowledge-based Information Engineering, Toyohashi University of Technology
  • KATO Hiroaki
    Dept. of Knowledge-based Information Engineering, Toyohashi University of Technology

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Other Title
  • 化学物質の構造類似性にもとづくデータマイニング
  • カガク ブッシツ ノ コウゾウ ルイジセイ ニ モトヅク データマイニング

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This paper describes an approach to chemical data mining based on the quantitative evaluation of structural similarity. The topological fragment spectrum (TFS) method reported by the authors was used for describing a chemical structure by numerical representation. The TFS is based on enumeration and numerical characterization of all possible substructures derived from the chemical structures. The TFS was applied to similar structure searching with over 3, 600 drugs extracted from the World Drug Index. All the spectra were characterized for fragments having five or less bonds. Five different similarity (or dissimilarity) functions were investigated for their suitability for similarity searching with the TFS. Computational trial of similar structure searching on the database suggested that the present approach is successfully applicable to chemical and pharmaceutical data mining based on the evaluation of structural similarity of drug molecules.

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