Ground truth explanation dataset for chemical property prediction on molecular graphs

Interpretation of chemistry on an atomic scale improves with explainable artificial intelligence (XAI). The parts of the molecule with the most significant influence on the chemical property of interest can be visualized with atomwise and bondwise attributions. Nonetheless, the attributions from different XAI methods regularly disagree substantially, causing uncertainty about which explainability is correct. To determine a ground truth for attributions, we define chemical operations which avoid alchemical steps or approximations and allow extracting one attribution per atom or bond from existing datasets of chemical properties. This general procedure allows generating large datasets of ground truth attributions. The approach allowed us to create a ground truth explanation dataset with more than 5 million data points for the HOMO-LUMO gap chemical property. This open-source dataset of atomistic ground truth explainability may serve as a reference for XAI approaches.