ArticleNature computational science2025
Iterative variational learning of committor-consistent transition pathways using artificial neural networks.
Article in Nature computational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Article
- Learning the reaction coordinate: collective variables from physical intuition to generative models.Digital discovery · 2026Review
- Free-Energy Landscapes of HBV Hexamer Closure Reveal Key Structural Features of the Transition.Journal of chemical theory and computation · 2026Article
- Rectifying AI-generated protein structure ensembles for equilibrium using physics-based computations.bioRxiv : the preprint server for biology · 2026Article
- Committors without Descriptors.Journal of chemical theory and computation · 2026Article
- Following the Committor Flow: A Data-Driven Discovery of Transition Pathways.Journal of chemical theory and computation · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- An Exact Multiple-Time-Step Variational Formulation for the Committor and the Transition Rate.The journal of physical chemistry. B · 2026Article
- More Sophisticated Is Not Always Better: A Comparison of Similarity Measures for Unsupervised Learning of Pathways in Biomolecular Simulations.The journal of physical chemistry. B · 2025Article
- The Arch from the Stones: Understanding Protein Folding Energy Landscapes via Bioinspired Collective Variables.The journal of physical chemistry letters · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Discovering transition pathways that are physically meaningful and committor-consistent has long been a challenge in studying rare events in complex systems. Here we introduce a neural network-based strategy that learns simultaneously the committor function and the associated committor-consistent string, offering an unprecedented view of transition processes. Built on the committor time-correlation function, this method operates across diverse dynamical regimes, and extends beyond traditional approaches relying on infinitesimal time-lag approximations, valid only in the overdamped diffusive limit. It also distinguishes multiple competing pathways, crucial for understanding complex biomolecular transformations. Demonstrated on benchmark potentials and biological systems such as peptide isomerization and protein-model folding, this approach robustly reproduces established dynamics, rate constants and transition mechanisms. Its adaptability to collective variables and resilience across neural architectures make it a powerful and versatile tool for enhanced-sampling simulations of rare events, enabling insights into the intricate landscapes of biomolecular 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.