SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems

Peptide sequence governs the structures and dynamics that emerge during supramolecular assembly, but predicting them requires resolving slow collective processes among many interacting molecules. Molecular dynamics (MD) provides microscopic insight, yet long assembly times and the vast peptide sequence space make systematic exploration costly. We introduce SupraTITO, a transferable generative molecular dynamics (GenMD) framework for supramolecular dynamics, demonstrated for peptide self-assembly. SupraTITO learns transferable implicit transfer operators (TITO) conditioned on peptide sequence, molecular topology, and periodic geometry, allowing configurations to be propagated over intervals much longer than an MD integration step. On a comprehensive dipeptide benchmark, SupraTITO generalizes to held-out sequences, reproduces sequence-dependent structures and dynamics, and maintains molecular integrity over long rollouts. Compared with direct ensemble prediction trained on the same trajectory data, it produces more accurate assembly structures while also recovering their temporal evolution. SupraTITO further generalizes across peptide concentrations, including dilute conditions absent from training. These results extend transferable GenMD to collective dynamics in periodic supramolecular systems and related processes beyond peptide assembly.