In recent years, graph neural networks (GNNs) have gained attention for their effectiveness in modeling various molecular and mixture properties . A key advantage of GNNs is that they are able to predict, directly from the molecular structure of mixture components (represented as graphs), properties of interest. In a molecular graph, the nodes and edges typically represent the non-hydrogen and covalent bonds of the molecule. Each node and edge in the graph encodes information regarding the corresponding atom or chemical bond. This information is propagated and updated using a message-passing scheme which, after a global pooling operation, allows for learning a vectorial graph representation that can be later used for a specific prediction task (e.g., regression or classification). In the context of multi-component systems, mixture representations include the simple concatenation of each of the component learned embeddings , and the modeling of relevant intermolecular interactions (e.g., hydrogen bonding) via the construction of an interaction or mixture graph . In this mixture graph, the nodes represent molecules and edges represent hydrogen bonding interactions. These types of graphs have been used for the estimation of temperature-dependent infinite dilution activity coefficients of organic molecules , polymer solutions , and ionic liquids . Such models have also been used for solvent pre-selection for extractive distillation .