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Data-driven enhanced sampling of mechanistic pathways.

Revanth Elangovan¹, Sompriya Chatterjee^1,2, Dhiman Ray¹

¹Department of Chemistry and Biochemistry, University of Oregon, Eugene, OR 97403.

Proceedings of the National Academy of Sciences of the United States of America

|December 4, 2025

Summary

This summary is machine-generated.

This study introduces a novel deep multitask learning algorithm to efficiently identify the minimum free energy pathway (MFEP) for molecular processes. This method simplifies pathway exploration and mechanistic characterization, reducing computational cost.

Keywords:

machine learning metadynamics minimum free energy pathway molecular dynamics multi-task learning

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Area of Science:

Computational Chemistry
Molecular Dynamics
Biophysics

Background:

Characterizing molecular mechanisms requires understanding minimum free energy pathways (MFEPs) on complex conformational landscapes.
High-dimensional free energy landscapes are computationally challenging to converge using current enhanced sampling methods.

Purpose of the Study:

To develop a computationally efficient algorithm for learning the MFEP without prior knowledge of the free energy landscape.
To simplify pathway exploration and enable automatic reconstruction of molecular mechanisms.

Main Methods:

Integration of deep neural networks with well-tempered metadynamics for iterative MFEP learning.
A simplified protocol that avoids the need for intermediate structures or guessed paths.

Main Results:

The deep multitask learning algorithm successfully learns MFEPs for chemical reactions, protein folding, and ligand-receptor binding.
The approach offers a lower computational cost compared to existing pathway exploration methods.
Automatic reconstruction of mechanistic fingerprints from learned pathways is demonstrated.

Conclusions:

The developed framework provides a simplified and computationally inexpensive method for elucidating molecular mechanisms.
This approach is expected to have broad applications in all-atom resolution molecular simulations.
It overcomes limitations of current methods in obtaining converged high-dimensional free energy landscapes.