ICML 2025

UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
Ziyang Yu 1 Wenbing Huang 2 3 Yang Liu 1 4
Abstract
Molecular Dynamics (MD) simulations are essen-
tial for understanding the atomic-level behavior of
molecular systems, giving insights into their tran-
sitions and interactions. However, classical MD
techniques are limited by the trade-off between ac-
curacy and efficiency, while recent deep learning-
based improvements have mostly focused on
single-domain molecules, lacking transferability
to unfamiliar molecular systems. Therefore, we
propose Unified Simulator (UniSim), which lever-
ages cross-domain knowledge to enhance the un-
derstanding of atomic interactions. First, we em-
ploy a multi-head pretraining approach to learn a
unified atomic representation model from a large
and diverse set of molecular data. Then, based on
the stochastic interpolant framework, we learn the
state transition patterns over long timesteps from
MD trajectories, and introduce a force guidance
module for rapidly adapting to different chemical
environments. Our experiments demonstrate that
UniSim achieves highly competitive performance
across small molecules, peptides, and proteins.
1. Introduction
Molecular Dynamics (MD) simulations, an in silico method
used to comprehend the time evolution of molecular sys-
tems in given environments, serve as a fundamental and
essential tool in various fields like computational chem-
istry, pharmacology, material design, and condensed mat-
ter physics (Van Gunsteren & Berendsen, 1990; Lindorff-
Larsen et al., 2011; Hollingsworth & Dror, 2018; Lau et al.,
*Equal contribution
1Department of Computer Science and
Technology, Tsinghua University, Beijing, China 2Gaoling School
of Artificial intelligence, Renmin University of China, Beijing,
China 3Engineering Research Center of Next-Generation Intelli-
gent Search and Recommendation, MOE 4Institute for AI Industry
Research (AIR), Tsinghua University, Beijing, China. Correspon-
dence to...