Explaining Deep Graph Networks with Molecular Counterfactuals
- URL: http://arxiv.org/abs/2011.05134v1
- Date: Mon, 9 Nov 2020 13:46:10 GMT
- Title: Explaining Deep Graph Networks with Molecular Counterfactuals
- Authors: Danilo Numeroso, Davide Bacciu
- Abstract summary: We present a novel approach to tackle explainability of deep graph networks in the context of molecule property prediction tasks, named MEG.
We generate informative counterfactual explanations for a specific prediction under the form of (valid) compounds with high structural similarity and different predicted properties.
- Score: 11.460692362624533
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a novel approach to tackle explainability of deep graph networks
in the context of molecule property prediction tasks, named MEG (Molecular
Explanation Generator). We generate informative counterfactual explanations for
a specific prediction under the form of (valid) compounds with high structural
similarity and different predicted properties. We discuss preliminary results
showing how the model can convey non-ML experts with key insights into the
learning model focus in the neighborhood of a molecule.
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