The best performing model employed four Graphormer encoder layers, each with an embedding dimension of 2100. A multiheaded attention mechanism was used with 210 attention heads and an attention dropout of 0.10. Adam was used as the optimizer with 𝛽 1 = 0.9, 𝛽 2 = 0.999, 𝜖 = 10 -8 , 56,250 warmup updates, and 375,000 total updates. The best performing model used a learning rate of 3.0 x 10 -5 , though models with less dense encodings (i.e., only atomic number as a node feature) performed better with learning rates of 6.0-7.0x10 -5 . The MLP consisted of three linear layers with an output of size 1800, interleaved with Rectified Linear Units (ReLUs) as the activation function. In cases where a 1D-CNN was tested, the last linear layer produced an output of size of 1840, followed by a 1D-CNN with a kernel size of 40. The loss function used in training was the spectral information divergence (SID), the metric that was used in the work of McGill, which is described in the Spectral Loss Function section above. In total, the best performing model had 138,700,619 tunable parameters, which represents a very wide but short model architecture. All hyperparameters for the best performing models can be found in tables S2 of the supplementary information.