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    Optimal Measurement Network of Pairwise Differences
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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2019, 59, 11, 4720–4728
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    https://doi.org/10.1021/acs.jcim.9b00528
    Published October 15, 2019
    Copyright © 2019 American Chemical Society
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    Abstract

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    When both the difference between two quantities and their individual values can be measured or computationally predicted, multiple quantities can be determined from the measurements or predictions of select individual quantities and select pairwise differences. These measurements and predictions form a network connecting the quantities through their differences. Here, I analyze the optimization of such networks, where the trace (A-optimal), the largest eigenvalue (E-optimal), or the determinant (D-optimal) of the covariance matrix associated with the estimated quantities are minimized with respect to the allocation of the measurement (or computational) cost to different measurements (or predictions). My statistical analysis of the performance of such optimal measurement networks—based on large sets of simulated data—suggests that they substantially accelerate the determination of the quantities and that they may be useful in applications such as the computational prediction of binding free energies of candidate drug molecules.

    Copyright © 2019 American Chemical Society

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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2019, 59, 11, 4720–4728
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jcim.9b00528
    Published October 15, 2019
    Copyright © 2019 American Chemical Society
    Request reuse permissions

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