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Related Experiment Videos

The relationship between molecular connectivity and partition coefficients.

J C Boyd, J S Millership, A D Woolfson

    The Journal of Pharmacy and Pharmacology
    |June 1, 1982
    PubMed
    Summary

    This study confirms a link between molecular connectivity and octanol-water partition coefficient (Log P) using experimental data. The findings validate previous research, employing updated connectivity indices for enhanced accuracy in predicting compound properties.

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    Area of Science:

    • * Physical Organic Chemistry
    • * Computational Chemistry
    • * Drug Discovery

    Background:

    • * Kier & Hall (1976) established a correlation between molecular connectivity and Log P (octanol-water partition coefficient).
    • * Previous studies relied on calculated Log P values, potentially limiting accuracy.
    • * The octanol-water partition coefficient is a critical parameter in predicting drug absorption, distribution, metabolism, and excretion (ADME).

    Purpose of the Study:

    • * To re-evaluate the relationship between molecular connectivity and experimentally determined Log P values.
    • * To validate and potentially refine the model proposed by Kier & Hall.
    • * To explore the utility of different connectivity indices in this relationship.

    Main Methods:

    • * Utilized experimentally determined Log P values for various compound classes.

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  • * Employed molecular connectivity indices to model the Log P relationship.
  • * Statistical analysis to confirm correlations and identify significant indices.

    • Main Results:

    • * The study confirms a significant relationship between molecular connectivity and experimental Log P values.
    • * The findings align with Kier & Hall's original observations.
    • * Specific connectivity indices were identified as important predictors in the refined equations.

    Conclusions:

    • * Molecular connectivity remains a valuable tool for predicting the octanol-water partition coefficient.
    • * Using experimental Log P data enhances the reliability of these predictions.
    • * The study provides an updated quantitative structure-property relationship (QSPR) model.