We implemented an end-to-end network based on the original implementation of Graphormer 46 that was built using the PyTorch Python package (see figure ) and was modified to improve predictions in this use case. Our model consists of a novel graph feature encoder (1 in figure ), that creates molecular graphs with node-(atom) and edge-(bond) level encodings using the DGL-LifeSci package. These graphs are passed to Graphormer (2 in figure ), which constructs a learned, dense molecular representation. More details on this architecture can be found in the work of Ying et al.. This dense molecular representation is then passed to the MLP (3 in figure ), which consists of intermediate linear layers and activation functions. We also tested the inclusion of a onedimensional convolutional neural network (1D-CNN) as the last layer of the MLP to smooth spectra (like the Gaussian convolution of McGill) and improve peak shapes. Pure model outputs suffered from point-to-point noise, since the model does not innately understand that adjacent points are related. By incorporating convolution, we hoped to test if smoothing peak predictions at each epoch could theoretically allowing the model to learn more realistic peak profiles through backpropagation. The results of this inclusion are discussed in the Ablation Study section below.