ArticleProceedings of the National Academy of Sciences of the United States of America2022
Likelihood-based non-Markovian models from molecular dynamics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Gillespie-based simulation and inference for non-Markovian stochastic reaction networks.Briefings in bioinformatics · 2026Article
- Article
- Physics-Inspired Single-Particle Tracking Accelerated with Parallelism.bioRxiv : the preprint server for biology · 2025Article
- Nonequilibrium relaxation exponentially delays the onset of quantum diffusion.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- On the emergence of machine-learning methods in bottom-up coarse-graining.Current opinion in structural biology · 2025Review
- Refining potential energy surface through dynamical properties via differentiable molecular simulation.Nature communications · 2025Article
- Accuracy of Reaction Coordinate Based Rate Theories for Modelling Chemical Reactions: Insights From the Thermal Isomerization in Retinal.Journal of computational chemistry · 2025Article
- Accurate Memory Kernel Extraction from Discretized Time-Series Data.Journal of chemical theory and computation · 2024Article
- Accurate estimates of dynamical statistics using memory.The Journal of chemical physics · 2024Article
- Building insightful, memory-enriched models to capture long-time biochemical processes from short-time simulations.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Pair-Reaction Dynamics in Water: Competition of Memory, Potential Shape, and Inertial Effects.The journal of physical chemistry. B · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
SignificanceThe analysis of complex systems with many degrees of freedom generally involves the definition of low-dimensional collective variables more amenable to physical understanding. Their dynamics can be modeled by generalized Langevin equations, whose coefficients have to be estimated from simulations of the initial high-dimensional system. These equations feature a memory kernel describing the mutual influence of the low-dimensional variables and their environment. We introduce and implement an approach where the generalized Langevin equation is designed to maximize the statistical likelihood of the observed data. This provides an efficient way to generate reduced models to study dynamical properties of complex processes such as chemical reactions in solution, conformational changes in biomolecules, or phase transitions in condensed matter systems.
Indexed as
Identifiers
What OpenQuestion holds
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.