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SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems

arXiv 2610.01381 ↗First posted 1 Oct 2026physics.chem-ph cond-mat.soft cs.LGcited by 0

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Abstract (arXiv)

Peptide sequence governs both the structures formed through supramolecular assembly and the dynamics by which they emerge, but predicting either requires resolving slow collective processes among many interacting molecules. Molecular dynamics (MD) provides microscopic insight into these processes, yet the long timescales of assembly and the vast peptide sequence space make systematic exploration computationally demanding. We introduce SupraTITO, a transferable generative molecular dynamics (GenMD) framework for supramolecular systems, demonstrated through 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 physical intervals much longer than an MD integration step. On a comprehensive dipeptide benchmark, SupraTITO generalizes to held-out sequences and reproduces sequence-dependent structures and dynamics while maintaining molecular integrity over long rollouts. Compared with direct ensemble prediction trained on the same trajectory data, SupraTITO more accurately reproduces assembly structures while also resolving their temporal evolution. The learned dynamics generalize across peptide concentrations, including dilute conditions not represented during training. These results extend transferable GenMD to collective dynamics in periodic supramolecular systems and provide a foundation for modeling related processes beyond peptide assembly.

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References · 16

  1. 2018Neural Ordinary Differential Equationscited by 3,733
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  6. 2023Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamicscited by 62
  7. 2023Ewald-based Long-Range Message Passing for Molecular Graphscited by 46
  8. 2025La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matchingcited by 26
  9. 2024EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolantscited by 15
  10. 2020Molecular Latent Space Simulatorscited by 13
  11. 2025Force-Free Molecular Dynamics Through Autoregressive Equivariant Networkscited by 13
  12. 2025Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamicscited by 8
4 more
  1. 2020Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulationcited by 6
  2. 2025Calibrating Generative Models to Distributional Constraintscited by 5
  3. 2026Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operatorscited by 3
  4. 2025Morphology-Aware Peptide Discovery via Masked Conditional Generative Modelingcited by 2

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