Investigating the Application of Interpretability Techniques to Computational Toxicology

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Date
2021Author
Banerjee, Aranya
Boby, Kevin
Lam, Samuel
Polefrone, David
San, Robert
Schlunk, Erika
Wynn, Sean
Yancey, Colin
DRUM DOI
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Show full item recordAbstract
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