The computational chemistry community has developed solid foundations for describing the quantummechanical nature of molecular structures. For example, it is well-known that molecules are not only composed of covalent interactions but also stabilizing noncovalent interactions between the corresponding bond and lone pair orbitals. However, these non-trivial interactions are not currently considered when designing computational representations of molecules, even though they play a crucial role in describing a wide range of properties. These stabilizing donor-acceptor interactions are essential in our understanding of many chemical phenomena and have been disregarded by the machine learning community. This work proposes a new approach to representing molecular structures with graphs that capture orbital interactions. We extend standard molecular structure graphs with additional nodes representing bonding and non-boning orbitals. Such information is then used to predict intra-molecular interactions that are not explicitly given by the original topology. This work describes learning features for these nodes from quantum chemistry calculations and extending original graphs improves over baselines.