NeurIPS 2025

JAMUN: Bridging Smoothed Molecular Dynamics and
Score-Based Learning for Conformational Ensemble
Generation
Ameya Daigavane∗
MIT
ameyad@mit.edu
Bodhi P. Vani∗
Prescient Design, Genentech
vanib@gene.com
Darcy Davidson
Prescient Design, Genentech
davidsd5@gene.com
Saeed Saremi
Prescient Design, Genentech
saremis@gene.com
Joshua A. Rackers†
Prescient Design, Genentech
Joseph Kleinhenz†
Prescient Design, Genentech
kleinhej@gene.com
Abstract
Conformational ensembles of protein structures are immensely important both for
understanding protein function and drug discovery in novel modalities such as
cryptic pockets. Current techniques for sampling ensembles such as molecular dy-
namics (MD) are computationally inefficient, while many recent machine learning
methods do not transfer to systems outside their training data. We propose JAMUN
which performs MD in a smoothed, noised space of all-atom 3D conformations of
molecules by utilizing the framework of walk-jump sampling. JAMUN enables
ensemble generation for small peptides at rates of an order of magnitude faster
than traditional molecular dynamics. The physical priors in JAMUN enables trans-
ferability to systems outside of its training data, even to peptides that are longer
than those originally trained on. Our model, code and weights are available at
https://github.com/prescient-design/jamun.
1
Introduction
Proteins are inherently dynamic entities constantly in motion, and these movements can be vitally
important. They are not well characterized as single structures as has traditionally been the case,
but rather as ensembles of structures drawn from the Boltzmann distribution [30]. Protein dynamics
is required for the function of most proteins, for instance the global tertiary structure motions for
myglobin to bind oxygen and move it around the body [57], or the beta-sheet transition to a disordered
strand for insulin to dissociate and find and bind to its receptor [5]. Similarly, drug discovery on
protein kinases depen...