Molecular representation learning In recent years, the field has greatly benefited from many novel representations of molecules in data-driven approaches. Ways to represent molecules include hand-crafted molecular descriptors/features, language representations, and graphs. Graph-based representations achieved state-of-the-art results by employing inherent topological structures of molecules in neural networks, known as Graph Neural Networks (GNNs). Moreover, recent advances combine GNNs with tridimensional geometry into roto-translation equivariant representations, increasing the amount of information to perform correlations. Graph augmentation Previous works have reported the difficulty of GNNs to propagate information across distant nodes. Such phenomena are often referred to as over-squashing, a major problem when node properties depend on long-range interactions. In molecular settings, these long-range interactions are often caused by noncovalent orbital interactions that are not explicitly given by the standard molecular topology. Several methods to mitigate this problem have been proposed, such as graph augmentations, fully-connected graph convolutional layers, and connectivity-independent long-range reasoning. To address this problem, we present a graph augmentation method grounded on electronic interactions. Electronic information on graphs Across the literature, physical and chemical priors have been successfully included in data-driven models. Usually, these methods use hand-crafted features from chemistry domain knowledge, showing major improvements over baselines. However, a recent effort has been made to model quantum interactions automatically within a molecule. In this work, we propose a molecular electronically-aware molecular featurization method that can be easily combined with existing representations.