Explainable machine learning is increasingly used in drug discovery to help rationalize compound property predictions. In this work we present a modification to the regression objective of GNNs to specifically account for common core structures between pairs of molecules. The proposed approach shows higher accuracy on a recently-proposed explainability benchmark.