Software Developers: Sagar Kania, Lisa Otten
Theory Collaborators: Robert J. Webber, Gideon Simpson, David Aristoff, and Daniel M. Zuckerman
- Paper: "Randomized iterative trajectory reweighting for steady-state distributions without discretization error" by Kania et al., PNAS 2026. https://www.pnas.org/doi/abs/10.1073/pnas.2529246123
- Preprint on smoothing: "Regularized RiteWeight for Sparse Trajectory Data: A Smoothed Stationary Reweighting Framework" by Otten et al., https://chemrxiv.org/doi/full/10.26434/chemrxiv.15001337/v1
- "bRiteWeight: Randomized iterative reweighting for biased trajectory data" by Aristoff and Zuckerman, https://chemrxiv.org/doi/full/10.26434/chemrxiv.15001675/v1
- Blog post on limitations: "Counting is not enough: A weakness of MSMs inherited by RiteWeight" by Zuckerman, https://statisticalbiophysicsblog.org/?p=566
rite_weight (RW) is a lightweight Python package for computing steady-state weights from short molecular dynamics (MD) trajectory segments. RW is not recommended for reweighting one or a small number of MD trajectories: see blog post noted above.
RW uses a simple yet powerful approach: at each iteration, the method randomly clusters a reduced feature space (e.g., PCA, tICA, VAMP) and constructs a Markov state model (MSM). It then reweights the trajectory based on the stationary distribution derived from the MSM transition matrix. The process is repeated with new random clusters until convergence. This randomized clustering approach allows RW to efficiently estimate long-timescale kinetics and thermodynamics from short simulations — without requiring long trajectories or predefined discrete states. The current software applies only to equilibrium reweighting, but RW is a general method (see paper) and nonequilibrium reweighting code will be released (planned by Fall 2026).
Install from github and update the existing conda env manually as:
git clone https://github.com/ZuckermanLab/rite_weight.git
cd </path/to/rite_weight>
conda activate
pip install .
Usage and Analysis with rite_weight Package
The example folder contains a demonstration of how to use the rite_weight package. The Jupyter notebook, example/run_riteweight.ipynb, illustrates how to build the model using data stored in the folder tests/. Additionally, the rite_weight_results_analysis.ipynb notebook provides examples of estimating pdf along a given CV.
Since the initial release, we have added a smoothing feature that allows for a more consistent analysis of sparse trajectory data.
We will be deploying features to enable kinetic estimation, such as residence time and mean first-passage time (MFPT). Uncertainty quantification will also be added. Stay tuned for updates.