The first node encoding scheme tested was atomic number only, to provide the maximum flexibility to Graphormer and to provide a baseline relative to models with additional features. Use of atomic node encodings yielded 𝑆𝐼𝑆 𝜇 = 0.8464 (n=1) and 𝑆𝐼𝑆 𝜇 = 0.8449 ± 0.0012 (n=5) which is 3.3σ better than the average result of Chemprop-IR with the full suite of features (𝑆𝐼𝑆 𝜇 = 0.8405 ± 0.0013). This result is even more meaningful when placed in context of the Chemprop-IR models that were trained on only atomistic encodings, which achieved an average score of 𝑆𝐼𝑆 𝜇 = 0.8326 ± 0.0015 (See Table ). These results indicate that graph transformers are more expressive and can derive sufficient understanding of bond frequencies from atomic number, bond types, and graph structure, using only 36% of the information found in the Chemprop-IR "full features" model. Table . Five-fold random cross validation results for the best performing models produced by Graphormer-IR and Chemprop-IR with only atomistic node encodings and with the full suite of node features as discussed in the Methods section. Both sets of models were tested on identical training, test, and validation splits.