Our HGAR model applies a couple of Graphormers to embed atomlevel and motif-level features jointly. The coupled Graphormers play a central role in the model. However, the Graphormers have a lot of parameters, while the size of the retrosynthesis dataset is limited. Therefore, training our HGAR model directly on the retrosynthesis dataset often leads to a severe over-fitting issue. To mitigate this problem, we pretrain a Graphormer on the PCQM4M-LSC dataset, which is a quantum chemistry dataset originally curated under the Pub-ChemQC project . The pretraining task is to predict the HOMO-LUMO energy gap given 2D molecular graphs, with Mean Absolute Error (MAE) as an evaluation metric. The dataset is split by Pub-Chem ID of molecules with a ratio 8/1/1, following the work in .