The prediction of molecular properties is at the core of chemical, biological, and material sciences. From the discovery of materials for solar panels to the record-setting development of a new drug, molecular machine learning (ML) greatly impacted modern science by enabling fast inference. The performance of ML methods in these fields is strongly connected to the molecular representation design, being (arguably) the most important factor for its success. Moreover, with the increasingly larger amount of available chemical data, many methods were developed to learn the best representation in a data-driven fashion. Especially, graph neural networks (GNNs) have been the protagonists of these advances by leveraging the molecular topology to correlate structure with the desired global property. Despite the success of machine learning for molecular property predictions, the representations used in these methods are incomplete. In particular, current graph representations lack quantum-chemical priors from the electronic structure of molecules. Such considerations are essential for understanding the fundamentals of molecular interactions, chemical reactivity, and property predictions. Even though these electronic features can be learned implicitly by molecular geometry, such a process requires large and expensive datasets. Moreover, there would be no guarantee that the pre-trained model would preserve all required information for downstream tasks.