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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