TSea: A Large-Scale Dataset of Synthetic Ternary Complexes for Molecular Glue Modeling

Molecular glues (MGs) are an emerging therapeutic modality that induces or stabilizes protein–protein interactions, with demonstrated clinical impact in oncology. However, the scarcity of experimentally resolved MG ternary structures severely limits data-driven molecular glue modeling. Here, we introduce **TSea**, a large-scale dataset comprising over 100,000 synthetic ternary-complex structures, more than 450 times the size of MG-PDB. Starting from experimentally determined protein–ligand complexes, we construct synthetic ternary complexes through structurally constrained protein-chain partitioning and contact-based screening against MG-PDB reference distributions. Comparative analyses show that TSea captures selected structural and interaction characteristics of MG-PDB. We further develop **TSeaDiff**, a diffusion-based molecular generator trained on TSea. Among the evaluated models, TSeaDiff achieves the closest agreement with MG-PDB in protein–ligand interaction profiles. These results support TSea as a scalable structural resource for data-driven molecular glue modeling.