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Knowledge Discovery on Chemical Reactivity from Experimental Reaction Information

  • Hiroko Satoh
  • Tadashi Nakata
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

A knowledge discovery approach from chemical information with focusing on negative information in positive data is described. Reported experimental chemical reactions are classified into some reaction groups according to similarities in physicochemical features with a self-organizing mapping (SOM) method. In one of the reaction groups, functional groups of reactants are divided into two categories according to the experimental results whether they reacted or not. The classes of the functional groups are used for derivation of knowledge on chemical reactivity and condition intensity. The approach is demonstrated with a model dataset.

Keywords

Knowledge Discovery Chemical Reactivity Condition Intensity Negative Information Positive Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hiroko Satoh
    • 1
    • 2
  • Tadashi Nakata
    • 2
  1. 1.Artificial Intelligence Systems DivisionNational Institute of InformaticsChiyoda, TokyoJapan
  2. 2.RIKENSynthetic Organic Chemistry LaboratoryWako, SaitamaJapan

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