  Explainable AI (XAI) is a research area whose objective is to increase
trustworthiness and to enlighten the hidden mechanism of opaque machine
learning techniques. This becomes increasingly important in case such models
are applied to the chemistry domain, for its potential impact on humans'
health, e.g, toxicity analysis in pharmacology. In this paper, we present a
novel approach to tackle explainability of deep graph networks in the context
of molecule property prediction t asks, 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. Given a trained DGN, we train a
reinforcement learning based generator to output counterfactual explanations.
At each step, MEG feeds the current candidate counterfactual into the DGN,
collects the prediction and uses it to reward the RL agent to guide the
exploration. Furthermore, we restrict the action space of the agent in order to
only keep actions that maintain the molecule in a valid state. We discuss the
results showing how the model can convey non-ML experts with key insights into
the learning model focus in the neighbourhood of a molecule.
