ArticleJournal of chemical theory and computation2025
Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding.
Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Convergence is not correctness: context-dependent performance of enhanced-sampling methods across biological complexity.Nature communications · 2026Article
- Deep learning insights into β-lactamase dynamics and resistance evolution.The Biochemical journal · 2026Review
- Generation of Pathway Signatures by Combining Normal Modes with Weighted Ensemble Simulations.bioRxiv : the preprint server for biology · 2026Article
- Rectifying AI-generated protein structure ensembles for equilibrium using physics-based computations.bioRxiv : the preprint server for biology · 2026Article
- Reducing weighted ensemble variance with optimal trajectory management.The Journal of chemical physics · 2026Article
- Weighted Ensemble Simulations Reveal Novel Conformations and Modulator Effects in Hepatitis B Virus Capsid Assembly.Journal of chemical theory and computation · 2025Article
- Weighted Ensemble Simulations With the Drude Polarizable Model.Journal of computational chemistry · 2025Article
- Rare-Event Sampling using a Reinforcement Learning-Based Weighted Ensemble Method.bioRxiv : the preprint server for biology · 2024Article
- Weighted Ensemble Simulation: Advances in methods, software, and applications.Wiley interdisciplinary reviews. Computational molecular scienceArticle
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Abstract
A major challenge for many rare-event sampling strategies is the identification of progress coordinates that capture the slowest relevant motions. Machine-learning methods that can identify progress coordinates in an unsupervised manner have therefore been of great interest to the simulation community. Here, we developed a general method for identifying progress coordinates "on-the-fly" during weighted ensemble (WE) rare-event sampling via deep learning (DL) of outliers among sampled conformations. Our method identifies outliers in a latent space model of the system's sampled conformations that is periodically trained using a convolutional variational autoencoder. As a proof of principle, we applied our DL-enhanced WE method to simulate the NTL9 protein folding process. To enable rapid tests, our simulations propagated discrete-state synthetic molecular dynamics trajectories using a generative, fine-grained Markov state model. Results revealed that our on-the-fly DL of outliers enhanced the efficiency of WE by >3-fold in estimating the folding rate constant. Our efforts are a significant step forward in the unsupervised learning of slow coordinates during rare event sampling.
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