From Prefix to Path: Learning Temporally Consistent Biomolecular Dynamics from Limited Initial Data

Abstract

Molecular dynamics (MD) simulations provide detailed insights into biomolecular motion but are often limited by the prohibitive cost of sampling long-timescale behavior. Here, we present a Transformer-based framework that reconstructs temporally continuous dynamical trajectories from only a small fraction of the initial data, directly targeting time-ordered evolution rather than independent ensemble snapshots. Using three systems spanning distinct dynamical regimes (intrinsically disordered -Synuclein, Cytochrome P450 ligand-binding motion, and a synthetic three-well potential), we show that the model learns both local fluctuations and long-range temporal structure. At inference time, the model generates full trajectories autoregressively from an initial prefix as prompt, capturing metastable transitions, basin-to-basin movements, and system-specific dynamical signatures. Free-energy surfaces computed from generated trajectories closely match ground-truth landscapes and, in several cases, we observe enhanced sampling in generated trajectories relative to the trained trajectories--while preserving kinetically meaningful transition patterns. These results demon-strate that Transformer architectures can serve as efficient, system-agnostic tools for time-continuous molecular trajectory prediction, offering a data-driven complement to long MD simulations and enabling accelerated exploration of conformational space.