Pharmacophore Refinement in the Chemical Structure Space

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  • FUJISHIMA Satoshi
    Department of Knowledge-based Information Engineering, Toyohashi University of Technology
  • TAKAHASHI Yoshimasa
    Department of Knowledge-based Information Engineering, Toyohashi University of Technology
  • OKADA Takashi
    Department of Informatics, School of Science and Technology, Kwansei Gakuin University

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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.

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