Deep learning approaches to property prediction have been performed on chemical structures, using as little as their SMILES strings to extract information independent of traditional molecular features . When compared to QSAR descriptors, which are 'fixed', these methods employ 'learned representations' of molecules for the model to separately 'learn' how such representations fit to the target variable(s). Addressing the issues related to graph propagation in conventional Graph neural networks (GNNs) , message-passing neural network (MPNN) models transfer information fewer steps along the molecule, which may translate to better performance for larger, more varied data sets . Furthermore, global properties which can be computed through conventional means have to be learnt from scratch in graph-based models. As such, our use of Chemprop was motivated by the optional loading of molecular properties on top of SMILES string inputs, in addition to its prior benchmarking with other descriptors . This therefore allows for bond-level information to be integrated with our descriptor-based methods.