ArticleNature communications2018
VAMPnets for deep learning of molecular kinetics.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 173 papers.
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Who cites it
173 citing papers in PubMed.
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- ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences.Nature communications · 2026Article
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- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Multiscale frameworks for exploring protein energy landscapes: advances in theory and simulation.Journal of biological physics · 2026Review
- DeepPath: overcoming data scarcity for protein transition pathway prediction using physics-based deep learning.Chemical science · 2026Article
- MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics.bioRxiv : the preprint server for biology · 2026Article
- Transformer-based framework uncovers state-dependent modular organization and conformational landscapes of the β-arrestin 1 C-terminal tail.bioRxiv : the preprint server for biology · 2026Article
- Generative Autoencoders Coupled to Monte Carlo Simulation Allow Efficient Protein Conformation Sampling.Journal of chemical theory and computation · 2026Article
- Functional Calmodulin States Are Selected from an Electrostatically Tuned Free Energy Landscape.Journal of chemical information and modeling · 2026Article
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- Dynamic mechanism for subtype selectivity of endocannabinoids.The Journal of biological chemistry · 2026Article
- Elucidating the Structure and Activation Mechanisms of GPCRs Using Modern Computational and AI Tools.Cell biochemistry and biophysics · 2026Review
- A Hydrophobic Cluster Modulates Long-Range Allostery in the TRMT2A RNA Recognition Motif.Journal of chemical information and modeling · 2026Article
- A Risk-Tiered Validation Framework for Artificial Intelligence in Drug Discovery: From Reproducibility to Clinical Translation.International journal of molecular sciences · 2026Review
- Analyzing Peptide Torsional Dynamics: An Angular-Displacement PCA Pipeline for Short-Horizon Prediction from Molecular Dynamics.The journal of physical chemistry. A · 2026Article
113 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.
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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.