The representation of molecular structures is crucial for molecular machine learning strategies. Although graph representations are highly versatile and show their broad applicability, they lack information about the quantum-chemical properties of molecular structures. This work proposes a new way to infuse such information into molecular graphs, using a supervised learning method. As a result, the model is able to predict essential higher-order interactions between electron-rich and electron-deficient localized orbitals. The learned interactions are then used as a representation for the prediction of downstream tasks, improving over QM9 baselines.