Sep 2025· Chemical Reviews· Vol 126, pp. 671 - 713· 58 citations· 327 references
MedicinePhysics
TL;DR
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Abstract
Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This Review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas, such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling.
A new paradigm that has recently emerged at the interface of machine learning and computational statistical physics: the use of generative models as tools for sampling through flexible probabilistic models that can assist the sampling of distributions known only up to a normalization constant is explored.
AdaptivePy is presented, a compact and extensible Python framework that implements nine seed-selection policies behind a single configuration-driven interface, spanning simple population-based baselines, several established machine-learning and geometry-based methods, and two ensemble or meta sampling policies introduc...
Hassan Nadeem, D. Kleiman, Diwakar Shukla· bioRxiv· 0 citations
Molecular dynamics (MD) is essential for investigating atomic‐scale processes in materials and molecular systems, but the cost of high‐accuracy machine learning force field simulations still limits accessible system sizes and timescales. Here, we propose a practical model‐switching strategy for Deep Potential (DP)‐ba...
R. Kanda, Megumu Yamazaki, Yuta Yoshimoto et al.· Advanced Intelligent Discove...· 0 citations
This work reports the development and integration of a Gaussian process model, adaptive, uncertainty-driven sampling scheme for enhanced sampling, which provides a generalizable strategy that can be extended to complex molecular processes.
It is demonstrated that accurate thermodynamic, kinetic, and mechanistic insights can be obtained with reasonable resource usage for processes occurring on time scales far beyond the reach of conventional MD simulations, even for processes occurring on time scales far beyond the reach of conventional MD simulations.
It is demonstrated that learned D* act as transferable, physically interpretable descriptors in skin permeability modeling, where their inclusion reduces test set error and improves correlation relative to models relying solely on conventional physicochemical descriptors.
Hojin Jung, Su-min Song, Sabari Kumar et al.· Journal of Chemical Informat...· 0 citations
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