mlsanityData to 14 Oct 2026

GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules

arXiv 2201.04609 ↗First posted 12 Jan 2022physics.comp-ph cs.LG physics.chem-phcited by 8

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Abstract (arXiv)

Finding low dimensional representation of data from long-timescale trajectories of biomolecular processes such as protein-folding or ligand-receptor binding is of fundamental importance and kinetic models such as Markov modeling have proven useful in describing the kinetics of these systems. Recently, an unsupervised machine learning technique called VAMPNet was introduced to learn the low dimensional representation and linear dynamical model in an end-to-end manner. VAMPNet is based on variational approach to Markov processes (VAMP) and relies on neural networks to learn the coarse-grained dynamics. In this contribution, we combine VAMPNet and graph neural networks to generate an end-to-end framework to efficiently learn high-level dynamics and metastable states from the long-timescale molecular dynamics trajectories. This method bears the advantages of graph representation learning and uses graph message passing operations to generate an embedding for each datapoint which is used in the VAMPNet to generate a coarse-grained representation. This type of molecular representation results in a higher resolution and more interpretable Markov model than the standard VAMPNet enabling a more detailed kinetic study of the biomolecular processes. Our GraphVAMPNet approach is also enhanced with an attention mechanism to find the important residues for classification into different metastable states.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 23 of its 70 references that are in the map. First posted on arXiv 12 Jan 2022.

Builds on

23 of 70 references.

References · 23

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11 more
  1. 2017Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelingcited by 137
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 4; 2026: 1.

8 papers.

  1. 2023Learning invariant representations of time-homogeneous stochastic dynamical systemscited by 17
  2. 2025Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systemscited by 7
  3. 2023Reaction coordinate flows for model reduction of molecular kineticscited by 6
  4. 2024Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamicscited by 5
  5. 2025Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Spacecited by 3
  6. 2025Hierarchical geometric deep learning enables scalable analysis of molecular dynamicscited by 1
  7. 2025Random functions as data compressors for machine learning of molecular processescited by 0
  8. 2026PIS: A Physics-Informed System for Accurate State Partitioning of Aβ42 Protein Trajectoriescited by 0