ArticleJournal of chemical theory and computation2023
Molecular Free Energies, Rates, and Mechanisms from Data-Efficient Path Sampling Simulations.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Enhanced Sampling of Protein Conformations in AlphaFold3 with Repulsive Bias in the Diffusion Generative Model.JACS Au · 2026Article
- Learning the reaction coordinate: collective variables from physical intuition to generative models.Digital discovery · 2026Review
- Molecular Dynamics Workflows to Compute Large-Scale Sets of Absolute Binding Free Energies Aiding Drug Candidate and Binding Pose Selection.Journal of chemical theory and computation · 2026Article
- The role of fluctuations in the nucleation process.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- STIM1 transmembrane helix dimerization captured by AI-guided transition path sampling.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding.Journal of chemical theory and computation · 2025Article
- Targeted TPS Shooting Using Computer Vision to Generate Ensemble of Trajectories.Journal of chemical theory and computation · 2025Article
- Free Energy, Rates, and Mechanism of Transmembrane Dimerization in Lipid Bilayers from Dynamically Unbiased Molecular Dynamics Simulations.The journal of physical chemistry. B · 2025Article
- Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles.Journal of chemical theory and computation · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular dynamics is a powerful tool for studying the thermodynamics and kinetics of complex molecular events. However, these simulations can rarely sample the required time scales in practice. Transition path sampling overcomes this limitation by collecting unbiased trajectories and capturing the relevant events. Moreover, the integration of machine learning can boost the sampling while simultaneously learning a quantitative representation of the mechanism. Still, the resulting trajectories are by construction non-Boltzmann-distributed, preventing the calculation of free energies and rates. We developed an algorithm to approximate the equilibrium path ensemble from machine-learning-guided path sampling data. At the same time, our algorithm provides efficient sampling, mechanism, free energy, and rates of rare molecular events at a very moderate computational cost. We tested the method on the folding of the mini-protein chignolin. Our algorithm is straightforward and data-efficient, opening the door to applications in many challenging molecular systems.
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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.