  In this work, we present an application of Locally Interpretable
Machine-Agnostic Explanations to 2-D chemical structures. Using this framework
we are able to provide a structural interpretation for an existing black-box
model for classifying biologically produced fuel compounds with regard to
Research Octane Number. This method of "painting" locally interpretable
explanations onto 2-D chemical structures replicates the chemical intuition of
synthetic chemists, allowing researchers in the field to directly accept,
reject, inform and evaluate decisions underlying inscrutably complex
quantitative structure-activity relationship models.
