GNNs are well-suited to supervised learning problems involving graph-structured data and align well with the task at hand of predicting features from a molecular structure. Previous studies on molecular descriptor prediction have found success employing 2D/3D graph inputs (i.e., without conformational ensemble information embedded) for properties such as chemical shift and bond dissociation energy. As such, we first aimed to evaluate a message-passing GNN employing 2D-input representations for our application to predict condensed descriptors encompassing the dynamic range of accessible conformers and their electronic, steric, and stereoelectronic properties.