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Discriminative structural analysis using pattern recognition techniques in the structure-taste problem of

Y Takahashi, Y Miyashita, Y Tanaka

    Journal of Pharmaceutical Sciences
    |June 1, 1984
    PubMed
    Summary

    Pattern recognition successfully predicted the taste of perillartine derivatives by analyzing molecular structure, hydrophobicity, and topological descriptors. This method accurately classified compounds, aiding in understanding structure-taste relationships.

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    Area of Science:

    • Medicinal Chemistry
    • Computational Chemistry
    • Cheminformatics

    Background:

    • Understanding structure-taste relationships is crucial for designing novel compounds.
    • Perillartine derivatives exhibit varying taste profiles that require explanation.
    • Predictive modeling can accelerate the discovery of compounds with desired sensory properties.

    Purpose of the Study:

    • To apply pattern recognition techniques to correlate the chemical structure of perillartine derivatives with their taste.
    • To develop a predictive model for classifying compounds as sweet or bitter based on their molecular descriptors.

    Main Methods:

    • Utilized hydrophobicity (log P) and water solubility (log S) as physicochemical descriptors.
    • Calculated topological descriptors, including fragment molecular connectivities, based on a template structure.

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  • Employed discriminant functions derived via simplex optimization for taste classification.

    • Main Results:

    • Hydrophobicity and specific topological descriptors significantly contributed to taste discrimination.
    • The developed discriminant function achieved high accuracy in classifying known compounds.
    • The model correctly predicted the taste class for 7 out of 9 previously unclassified compounds.

    Conclusions:

    • Pattern recognition effectively links molecular structure to taste in perillartine derivatives.
    • Hydrophobicity and key topological features are critical determinants of sweetness or bitterness.
    • This approach offers a valuable tool for predicting and designing taste profiles of chemical compounds.