ArticleProceedings of the National Academy of Sciences of the United States of America2017
Intrinsic map dynamics exploration for uncharted effective free-energy landscapes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- STIM1 transmembrane helix dimerization captured by AI-guided transition path sampling.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A beginner's approach to deep learning applied to VS and MD techniques.Journal of cheminformatics · 2025Review
- Free Energy, Rates, and Mechanism of Transmembrane Dimerization in Lipid Bilayers from Dynamically Unbiased Molecular Dynamics Simulations.The journal of physical chemistry. B · 2025Article
- Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles.Journal of chemical theory and computation · 2024Article
- Locating saddle points using gradient extremals on manifolds adaptively revealed as point clouds.Chaos (Woodbury, N.Y.) · 2023Article
- Visualization of Self-Assembly and Hydration of a β-Hairpin through Integrated Small and Wide-Angle Neutron Scattering.Biomacromolecules · 2023Article
- Spectral Map: Embedding Slow Kinetics in Collective Variables.The journal of physical chemistry letters · 2023Article
- Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution.Scientific reports · 2023Article
- Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations.Journal of chemical theory and computation · 2022Article
- Temporally Coherent Backmapping of Molecular Trajectories From Coarse-Grained to Atomistic Resolution.The journal of physical chemistry. A · 2022Article
- Assembling Biocompatible Polymers on Gold Nanoparticles: Toward a Rational Design of Particle Shape by Molecular Dynamics.ACS omega · 2022Article
- Sampling rare conformational transitions with a quantum computer.Scientific reports · 2022Article
- On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability.PNAS nexus · 2022Article
- Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures.The journal of physical chemistry. B · 2021Article
- CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting.Journal of chemical information and modeling · 2021Article
- Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.Chemical reviews · 2021Article
- Unsupervised Learning Methods for Molecular Simulation Data.Chemical reviews · 2021Article
- Machine-learned potentials for next-generation matter simulations.Nature materials · 2021Review
- Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks.Journal of chemical theory and computation · 2021Article
- Frontiers in Multiscale Modeling of Photoreceptor Proteins.Photochemistry and photobiology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.
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.