MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks
This addresses the need for trustworthy AI in chemistry, particularly for toxicity analysis in pharmacology, by offering a novel explanation method for deep graph networks.
The paper tackles explainability for deep graph networks in molecule property prediction by introducing MEG, a reinforcement learning-based method that generates valid molecular counterfactuals with high structural similarity and different predicted properties, achieving results that provide key insights for non-ML experts.
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.