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Database mining using soft computing techniques. An integrated neural network-fuzzy logic-genetic algorithm approach.

T R Cundari1, M Russo

  • 1Department of Chemistry, Computational Research on Materials Institute, The University of Memphis, Memphis, Tennessee 38152-6060, USA.

Journal of Chemical Information and Computer Sciences
|March 30, 2001
PubMed
Summary
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Soft computing techniques revealed relationships between bond lengths and angles in transition metal imido complexes. These methods identified known and new correlations, aiding chemical understanding and outlier detection.

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Informatics

Background:

  • Transition metal imido complexes (LnMdNZ) exhibit complex structural relationships.
  • Understanding these relationships is crucial for predicting chemical properties and reactivity.

Purpose of the Study:

  • To analyze metric parameters in transition metal imido complexes using soft computing (SC) techniques.
  • To identify and quantify relationships between bond lengths and angles within these complexes.
  • To explore potential novel chemical insights and database outliers.

Main Methods:

  • Employed two SC techniques: competitive learning neural network and a neural network-fuzzy logic-genetic algorithm approach.
  • Analyzed a subset of data from the Cambridge Structural Database.

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  • Utilized a fuzzy inference system to construct a multi-variable hypersurface.

    • Main Results:

    • Confirmed and quantified a suspected inverse correlation between metal-nitrogen bond length and metal-nitrogen-carbon bond angle.
    • Discovered an unexpected inverse correlation between the nitrogen-carbon (NC) distance and the metal-nitrogen-carbon (MNC) angle.
    • Identified an 'outlier' cluster, potentially indicating experimental errors or novel chemical structures.
    • Observed that alkyl versus aryl substituents influence these relationships.

    Conclusions:

    • SC techniques effectively mine chemical databases to confirm known and discover unknown structural correlations.
    • The identified hypersurface highlights complex interdependencies between NC, MNred, and MNC parameters.
    • The detection of outliers underscores the value of SC in identifying data anomalies and potential new chemical entities.