A wide range of machine learning (ML) approaches allows for explaining the chemistry of molecules, attributing which parts of the molecules are responsible for the chemical property of interest , and lessening the black box challenge of machine learning . Typical explainable ML approaches that provide atomwise attribution include dummy atoms , classification of atoms by chemical intuition , regression models , graph neural network (GNN) attributions with gradients , perturbations , decompositions , and surrogates . In contrast, fragment-based explainability approaches generate importance values for groups of atoms or functional groups (subgraphs), e.g., Hammett equation , matched molecular pairs , molecular scaffolds , and counterfactuals . Due to the ambiguity in molecule fragment identification (e.g., overlapping fragments) , this work considers only atomwise (nodes) and bondwise (edges) attributions, where one explanation value (attribution) is generated for each atom or bond.