Our experimental dataset was then restructured such that the format was compatible with the MPNN infrastructure. Five training, validation, and test splits were generated that were identical to those used in the training of Graphormer-IR. Models were trained using the hyperparameters of the most successful models reported in reference 37 and are available in table S2. Any deviation from the original evaluation procedure made in Graphormer-IR (evaluation from 500-4,000 cm -1 , 15 cm -1 Gaussian convolution) was also propagated back into the Chemprop-IR architecture. Only single model performances were considered for both Graphormer and Chemprop. MPNN models were tested using two sets of node-level features: only atomic number, and the complete suite of node features described in reference 38.