Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations

Jeet Mohapatra, Nima Dehmamy, Csaba Both, Subhro Das, Tommi Jaakkola
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:44594-44614, 2025.

Abstract

We introduce a novel approach for discovering effective degrees of freedom (DOF) in molecular dynamics simulations by mapping the DOF to approximate symmetries of the energy landscape. Unlike most existing methods, we do not require trajectory data but instead rely on knowledge of the forcefield (energy function) around the initial state. We present a scalable symmetry loss function compatible with existing force-field frameworks and a Hessian-based method efficient for smaller systems. Our approach enables systematic exploration of conformational space by connecting structural dynamics to energy landscape symmetries. We apply our method to two systems, Alanine dipeptide and Chignolin, recovering their known important conformations. Our approach can prove useful for efficient exploration in molecular simulations with potential applications in protein folding and drug discovery.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-mohapatra25a, title = {Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations}, author = {Mohapatra, Jeet and Dehmamy, Nima and Both, Csaba and Das, Subhro and Jaakkola, Tommi}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {44594--44614}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/mohapatra25a/mohapatra25a.pdf}, url = {https://proceedings.mlr.press/v267/mohapatra25a.html}, abstract = {We introduce a novel approach for discovering effective degrees of freedom (DOF) in molecular dynamics simulations by mapping the DOF to approximate symmetries of the energy landscape. Unlike most existing methods, we do not require trajectory data but instead rely on knowledge of the forcefield (energy function) around the initial state. We present a scalable symmetry loss function compatible with existing force-field frameworks and a Hessian-based method efficient for smaller systems. Our approach enables systematic exploration of conformational space by connecting structural dynamics to energy landscape symmetries. We apply our method to two systems, Alanine dipeptide and Chignolin, recovering their known important conformations. Our approach can prove useful for efficient exploration in molecular simulations with potential applications in protein folding and drug discovery.} }
Endnote
%0 Conference Paper %T Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations %A Jeet Mohapatra %A Nima Dehmamy %A Csaba Both %A Subhro Das %A Tommi Jaakkola %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-mohapatra25a %I PMLR %P 44594--44614 %U https://proceedings.mlr.press/v267/mohapatra25a.html %V 267 %X We introduce a novel approach for discovering effective degrees of freedom (DOF) in molecular dynamics simulations by mapping the DOF to approximate symmetries of the energy landscape. Unlike most existing methods, we do not require trajectory data but instead rely on knowledge of the forcefield (energy function) around the initial state. We present a scalable symmetry loss function compatible with existing force-field frameworks and a Hessian-based method efficient for smaller systems. Our approach enables systematic exploration of conformational space by connecting structural dynamics to energy landscape symmetries. We apply our method to two systems, Alanine dipeptide and Chignolin, recovering their known important conformations. Our approach can prove useful for efficient exploration in molecular simulations with potential applications in protein folding and drug discovery.
APA
Mohapatra, J., Dehmamy, N., Both, C., Das, S. & Jaakkola, T.. (2025). Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:44594-44614 Available from https://proceedings.mlr.press/v267/mohapatra25a.html.

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