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Related Experiment Videos

Physiochemical-activity relations in practice. 1. A rational and self-consistent data bank.

F E Norrington, R M Hyde, S G Williams

    Journal of Medicinal Chemistry
    |June 1, 1975
    PubMed
    Summary

    This study presents a comprehensive data bank of substituent constants for physicochemical-activity relations (PAR) studies. The data includes electronic and bulk parameters for various substituent positions, offering a valuable resource for researchers.

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    Area of Science:

    • Medicinal Chemistry
    • Quantitative Structure-Activity Relationships (QSAR)
    • Physical Organic Chemistry

    Background:

    • Physicochemical-activity relations (PAR) are crucial for understanding drug design and chemical reactivity.
    • Accurate substituent constants are essential for developing reliable PAR models.
    • Existing data may have gaps or high interparameter correlations, limiting their utility.

    Purpose of the Study:

    • To create a comprehensive and reliable data bank of substituent constants.
    • To provide parameters for physicochemical-activity relations (PAR) studies.
    • To facilitate the development of new predictive models in chemistry and pharmacology.

    Main Methods:

    • Compilation of substituent constants for 26 ortho and 34 meta and para benzenoid substituents.

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  • Inclusion of distributive parameters (pri and pri-), a bulk parameter (molar refraction), and electronic parameters (F and R).
  • Systematic listing of parameters for three substituent positions.

    • Main Results:

    • A complete data bank with no missing values across all listed parameters and substituent positions.
    • Low interparameter correlations, indicating the distinctiveness of the provided parameters.
    • A robust dataset suitable for advanced quantitative structure-activity relationship (QSAR) modeling.

    Conclusions:

    • The presented data bank offers a valuable and gapless resource for PAR and QSAR studies.
    • The low interparameter correlation enhances the reliability and interpretability of models built using this data.
    • This resource is expected to advance the field of predictive chemistry and drug discovery.