We implement all the methods in PyTorch and use the software RDKit to preprocess molecules and obtain their canonical SMILES strings. For our method, each Graphormer contains twelve 32-head attention layers, and the embedding size is set as 768. The motif vocabulary size is |X | = 758. We train our HGAR model for 50 epochs with a batch size of 96 and a learning rate of 0.0006 with AdamW optimizer on a single NVIDIA A6000 GPU.