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    Investigating the Application of Interpretability Techniques to Computational Toxicology

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    TOXIC_Thesis_FINALv2.pdf (519.0Kb)
    No. of downloads: 150

    Date
    2021
    Author
    Banerjee, Aranya
    Boby, Kevin
    Lam, Samuel
    Polefrone, David
    San, Robert
    Schlunk, Erika
    Wynn, Sean
    Yancey, Colin
    DRUM DOI
    https://doi.org/10.13016/sobq-is11
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    Abstract
    A barrier to the incorporation of predictive models for drug design lies in their lack of interpretability. To this end, we examine on three fronts the interpretability of benchmark models for the 2014 Tox21 Data Challenge, an initiative in the domain with a dataset of measurements across twelve toxicity experiments. On existing measures of model performance, we assess the current benchmark metrics' ability to describe model behavior and recommend an alternative set of metrics for the task. On the existing interpretability methods for machine learning models, we quantitatively and qualitatively evaluate their application to this domain by measuring desirable properties of explanations they produce. Additionally, we incorporate a recently described method for partial charge prediction as novel input for a toxicological model and observe its resulting model performance and model interpretability.
    Notes
    Gemstone Team TOXIC
    URI
    http://hdl.handle.net/1903/27076
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    DRUM is brought to you by the University of Maryland Libraries
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