ReviewChemical reviews2026
Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.
Review in Chemical reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Computational Methods for Molecular Dynamics of Supercooled Water Between 200 and 273 K.Entropy (Basel, Switzerland) · 2026Review
- Let's Stalk about Membranes: Committor-Based Enhanced Sampling of Stalk Formation.The journal of physical chemistry letters · 2026Article
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Learning the reaction coordinate: collective variables from physical intuition to generative models.Digital discovery · 2026Review
- Atomistic Simulations Decode the Mechanisms of DNA and RNA Processing Enzymes: Function through Motion.Accounts of chemical research · 2026Article
- Conformational landscape of 2-aminopurine-substituted RNA oligonucleotides from machine-learning-driven enhanced sampling.Physical chemistry chemical physics : PCCP · 2026Article
- Unraveling the Mechanism of Drug Binding to SARS-CoV-2 RNA Pseudoknot With Thermodynamics-Driven Machine Learning.Journal of computational chemistry · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.Nature communications · 2026Article
- Protonation and magnesium ions shape the transition state diversity of phosphoanhydride hydrolysis in water.Nature communications · 2026Article
- Toward Accurate RNA Folding Thermodynamics: Evaluation of Enhanced Sampling Methods for Force Field Benchmarking.Journal of chemical theory and computation · 2026Article
- Determination of coverage-dependent surface reaction rates using λ-dynamics: Application to molecular desorption.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Article
- Mechanistic Dissection of Conformational Transition of Bicyclic Peptide via Molecular Modeling and Deep Learning.bioRxiv : the preprint server for biology · 2026Article
- Committors without Descriptors.Journal of chemical theory and computation · 2026Article
- Integrated multi-omics analysis reveals distinct microbiota-metabolite signatures and a novel HCN2-2-hydroxybutyric acid interaction in inflammatory bowel disease.Frontiers in nutrition · 2026Article
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- Molecular dynamics simulation in traditional Chinese medicine research: from molecular mechanisms to multiscale validation.Frontiers in chemistry · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.