ArticleProceedings of the National Academy of Sciences of the United States of America2021
Deep learning the slow modes for rare events sampling.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.
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84 citing papers in PubMed.
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Atomistic Simulations Decode the Mechanisms of DNA and RNA Processing Enzymes: Function through Motion.Accounts of chemical research · 2026Article
- PETIMOT: a novel framework for inferring protein motions from sparse data using SE(3)-equivariant graph neural networks.Acta crystallographica. Section D, Structural biology · 2026Article
- Conformational landscape of 2-aminopurine-substituted RNA oligonucleotides from machine-learning-driven enhanced sampling.Physical chemistry chemical physics : PCCP · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- DeepPath: overcoming data scarcity for protein transition pathway prediction using physics-based deep learning.Chemical science · 2026Article
- Bayesian Umbrella Quadrature Automates Free-Energy Calculations Across Diverse Molecular Systems and Processes.Journal of chemical theory and computation · 2026Article
- Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.Nature communications · 2026Article
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- Transient tertiary structure in intrinsically disordered proteins revealed by multithermal enhanced sampling.Nature communications · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Unveiling hidden intermediate states in protein folding with AI-based conditional transition clustering.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Making PLUMED Fly: A Tutorial on Optimizing Performance.The journal of physical chemistry. B · 2026Article
- Latent thermodynamic flows: unified representation learning and generative modeling of temperature-dependent behaviors from limited data.Chemical science · 2026Article
- Random Functions as Data Compressors for Machine Learning of Molecular Processes.Journal of chemical theory and computation · 2026Article
- The seeds of the future are in the present: A blind exploration of metastable states.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Decoding Allosteric Inhibition in MALT1: The Hidden Role of Conformational Plasticity in Metastable States via Biased MD and Deep Learning.The journal of physical chemistry. B · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- Efficient and accurate spatial mixing of machine learned interatomic potentials for materials science.npj computational materials · 2026Article
- Data-driven enhanced sampling of mechanistic pathways.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
24 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system's modes that most slowly approach equilibrium under the action of the sampling algorithm. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on the fly probability-enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach, we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a miniprotein and the study of materials crystallization.
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