<i>π</i> -Girsanov: A generalized method to construct Markov state models from non-equilibrium and multiensemble biased simulations

We introduce π-Girsanov, a new method for constructing Markov state models from biased enhanced-sampling molecular dynamics simulations based on Girsanov reweighting. The key idea behind this new method is to separate the reweighting of the stationary density from the reweighting of the correlation function. We evaluate the effectiveness of this approach on several analytical potentials and on a model biomolecular system, comparing its performance with the original method. Our results show that π-Girsanov not only improves the estimation in a single-ensemble setting but also resolves key challenges in estimating transition matrices from multiensemble and non-equilibrium biased trajectories. Overall, π-Girsanov represents a substantial advance in kinetic reweighting, strengthening the connection between enhanced sampling techniques and Markov state modeling.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2026-08-18
DOI
https://doi.org/10.1063/5.0335247
Primary Topic
Protein Structure and Dynamics
Type
article
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article

π -Girsanov: A generalized method to construct Markov state models from non-equilibrium and multiensemble biased simulations

Hao Wu, Bettina G. Keller, Yong Wang, Mingyuan Zhang
The Journal of Chemical Physics
Protein Structure and Dynamics
article

π -Girsanov: A generalized method to construct Markov state models from non-equilibrium and multiensemble biased simulations

Hao Wu, Bettina G. Keller, Yong Wang, Mingyuan Zhang
article en

Abstract

We introduce π-Girsanov, a new method for constructing Markov state models from biased enhanced-sampling molecular dynamics simulations based on Girsanov reweighting. The key idea behind this new method is to separate the reweighting of the stationary density from the reweighting of the correlation function. We evaluate the effectiveness of this approach on several analytical potentials and on a model biomolecular system, comparing its performance with the original method. Our results show that π-Girsanov not only improves the estimation in a single-ensemble setting but also resolves key challenges in estimating transition matrices from multiensemble and non-equilibrium biased trajectories. Overall, π-Girsanov represents a substantial advance in kinetic reweighting, strengthening the connection between enhanced sampling techniques and Markov state modeling.

The Journal of Chemical PhysicsVol. 165(7)
Institute of Natural Science (KP), Universitat de Barcelona (ES), Freie Universität Berlin (DE), Zhejiang University (CN)
Openalex Percentile: Top 16%
Protein Structure and Dynamics
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