  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.
