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Selection of test series by a modified multidimensional mapping method.

R Wootton

    Journal of Medicinal Chemistry
    |February 1, 1983
    PubMed
    Summary
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    A new multidimensional mapping method ensures well-spread compound parameters while minimizing multicollinearity. This approach overcomes limitations of existing methods, leading to more robust datasets for chemical research.

    Area of Science:

    • Computational chemistry
    • Cheminformatics
    • Drug discovery

    Background:

    • Multidimensional mapping is crucial for selecting diverse compound sets.
    • Existing methods can inadvertently introduce multicollinearity, affecting data quality.
    • High multicollinearity complicates the interpretation of structure-activity relationships.

    Purpose of the Study:

    • To introduce and evaluate an alternative multidimensional mapping method.
    • To address the issue of multicollinearity in parameter space mapping.
    • To improve the selection of diverse and statistically sound compound libraries.

    Main Methods:

    • Developed a novel 'multidimensional mapping by distance and determinant' method.
    • Incorporated a criterion to maximize the determinant of the interparameter correlation matrix.

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  • Maintained a minimum distance criterion between compounds in the parameter space.

    • Main Results:

    • The new method effectively minimizes multicollinearity in parameter sets.
    • Performance comparison showed superior handling of collinearities compared to other methods.
    • Demonstrated the ability to maintain low levels of multicollinearity.

    Conclusions:

    • Multidimensional mapping by distance and determinant is a superior method for compound selection.
    • This technique enhances the quality and interpretability of chemical datasets.
    • It offers a robust solution for generating diverse compound libraries with minimal parameter correlation.