Pharmacophore Refinement in the Chemical Structure Space

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Studies on the structure–activity relationship of drugs essentially require a relational learning scheme in order to extract meaningful chemical subgraphs; however, most relational learning systems suffer from a vast search space. On the other hand, some propositional logic mining methods use the presence or absence of chemical fragments as features, but rules so obtained give only crude knowledge about part of the pharmacophore structure. This paper proposes a knowledge refinement method in the chemical structure space for the latter approach. A simple hill-climbing approach was shown to be very useful if the seed fragment contains the essential characteristic of the pharmacophore. An application to the analysis of dopamine D1 agonists is discussed as an illustrative example.

収録刊行物

  • Journal of computer chemistry, Japan  

    Journal of computer chemistry, Japan 7(2), 63-70, 2008-06-15 

    Society of Computer Chemistry, Japan

参考文献:  13件

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各種コード

  • NII論文ID(NAID)
    10021072248
  • NII書誌ID(NCID)
    AA11657986
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    13471767
  • NDL 記事登録ID
    9565135
  • NDL 雑誌分類
    ZP1(科学技術--化学・化学工業) // ZM13(科学技術--科学技術一般--データ処理・計算機)
  • NDL 請求記号
    Z74-C857
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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