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.
@article{arxiv.1611.07443,
title = {Mapping chemical performance on molecular structures using locally interpretable explanations},
author = {Leanne S. Whitmore and Anthe George and Corey M. Hudson},
journal= {arXiv preprint arXiv:1611.07443},
year = {2016}
}
Comments
Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems