Graph Transformers have demonstrated superiority in handling complex heterogeneous graphs in recent years. However, existing models still face several inherent challenges: (1) reliance on manually designed meta-paths to encode explicit local graph heterogeneity; (2) inability to capture fine-grained global information from distant yet relevant nodes. To address these limitations, we introduce THFormer, a novel node tokenized heterogeneous graph Transformer that learns expressive node representations by incorporating local and global perspectives. From the local perspective, we employ multiple subsequences for different heterogeneous types to explicitly encode local semantic relations, eliminating the need for manually designed meta-paths. From the global perspective, we design a local masking and global sampling mechanism to construct global structural (semantic) sequences, effectively capturing fine-grained global structural (semantic) information. Subsequently, THFormer separately feeds the resulting global and local sequences into standard Transformer layers as model inputs. Since these sequences represent two distinct views of the same target node, their corresponding outputs are naturally aligned to generate self-supervisory signals for model training, further enhancing the expressiveness and reliability of the target node representation. Extensive experiments are conducted to validate the efficacy of THFormer, and the quantitative performance gains are 0.24%, 0.31%, 0.51%, and 0.81% on DBLP, ACM, IMDB, and Freebase, respectively. The experimental results demonstrate the superiority of THFormer over representative heterogeneous graph neural networks and graph Transformer models.

Molecular docking plays a crucial role in predicting the binding mode of ligands to target proteins, and covalent interactions, which involve the formation of a covalent bond between the ligand and the target, are particularly valuable due to their strong, enduring binding nature. However, most existing docking methods and deep learning approaches hardly account for the formation of covalent bonds and the associated structural changes. To address this gap, we introduce a comprehensive benchmark for covalent docking, CovDocker, which is designed to better capture the complexities of covalent binding. We decompose the covalent docking process into three main tasks: reactive location prediction, covalent reaction prediction, and covalent docking. By adapting state-of-the-art models, such as Uni-Mol and Chemformer, we establish baseline performances and demonstrate the effectiveness of the benchmark in accurately predicting interaction sites and modeling the molecular transformations involved in covalent binding. These results confirm the role of the benchmark as a rigorous framework for advancing research in covalent drug design. It underscores the potential of data-driven approaches to accelerate the discovery of selective covalent inhibitors and addresses critical challenges in therapeutic development.