In recent years, a variety of methods to explain GNNs have been developed , sometimes combined with methods to improve the graph encoding for interpretability . However, most of these methods often emphasize node classification over the more relevant graph classification for chemists. A notable exception to this is EdgeSHAPer . EdgeSHAPer, like SHAP, makes use of the Shapley value from game theory but was developed with the explicit goal of drug discovery in mind, which is reflected in the author's proof-of-concept on activity predictions. The Shapley value has been used in the explanations of GNNs before , however, the EdgeSHAPer approach is novel in that the explanations focus on the edges instead of the nodes.