ArticleThe journal of physical chemistry. B2020
Integrated Variational Approach to Conformational Dynamics: A Robust Strategy for Identifying Eigenfunctions of Dynamical Operators.
Article in The journal of physical chemistry. B, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Hierarchical geometric deep learning enables scalable analysis of molecular dynamics.The Journal of chemical physics · 2026Article
- Adaptive Tensor Train Metadynamics for High-Dimensional Free Energy Exploration.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
- Using Multiple Isotope-Labeled Infrared Spectra for the Structural Characterization of an Intrinsically Disordered Peptide.The journal of physical chemistry. B · 2025Article
- Reaction Coordinates Are Optimal Channels of Energy Flow.Annual review of physical chemistry · 2025Review
- Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics.The Journal of chemical physics · 2025Article
- Article
- Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes.The Journal of chemical physics · 2024Article
- Accurate estimates of dynamical statistics using memory.The Journal of chemical physics · 2024Article
- Dynamics of activation in the voltage-sensing domain of Ciona intestinalis phosphatase Ci-VSP.Nature communications · 2024Article
- Integrative generalized master equation: A method to study long-timescale biomolecular dynamics via the integrals of memory kernels.The Journal of chemical physics · 2023Article
- EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.Briefings in bioinformatics · 2023Article
- Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction.The Journal of chemical physics · 2023Article
- EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.bioRxiv : the preprint server for biology · 2023Article
- Chasing collective variables using temporal data-driven strategies.QRB discovery · 2023Article
- Transition rate theory, spectral analysis, and reactive paths.The Journal of chemical physics · 2022Article
- Identifying signatures of proteolytic stability and monomeric propensity in O-glycosylated insulin using molecular simulation.Journal of computer-aided molecular design · 2022Article
- Structural Ensemble of the Insulin Monomer.Biochemistry · 2021Article
- TYROSINE KINASES: COMPLEX MOLECULAR SYSTEMS CHALLENGING COMPUTATIONAL METHODOLOGIES.The European physical journal. B · 2021Article
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5 authors.
Funding
Abstract
One approach to analyzing the dynamics of a physical system is to search for long-lived patterns in its motions. This approach has been particularly successful for molecular dynamics data, where slowly decorrelating patterns can indicate large-scale conformational changes. Detecting such patterns is the central objective of the variational approach to conformational dynamics (VAC), as well as the related methods of time-lagged independent component analysis and Markov state modeling. In VAC, the search for slowly decorrelating patterns is formalized as a variational problem solved by the eigenfunctions of the system's transition operator. VAC computes solutions to this variational problem by optimizing a linear or nonlinear model of the eigenfunctions using time series data. Here, we build on VAC's success by addressing two practical limitations. First, VAC can give poor eigenfunction estimates when the lag time parameter is chosen poorly. Second, VAC can overfit when using flexible parametrizations such as artificial neural networks with insufficient regularization. To address these issues, we propose an extension that we call integrated VAC (IVAC). IVAC integrates over multiple lag times before solving the variational problem, making its results more robust and reproducible than VAC's.
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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.