A self-learning algorithm for biased molecular dynamics.
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Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and DynamicsQM/MM molecular dynamics studies of metal binding proteinsP-loop conformation governed crizotinib resistance in G2032R-mutated ROS1 tyrosine kinase: clues from free energy landscapeEnhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolinConformational Transition Pathways of Epidermal Growth Factor Receptor Kinase Domain from Multiple Molecular Dynamics Simulations and Bayesian Clustering.Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables.From the Cover: Simplifying the representation of complex free-energy landscapes using sketch-map.Locating landmarks on high-dimensional free energy surfaces.Using sketch-map coordinates to analyze and bias molecular dynamics simulationsLocating binding poses in protein-ligand systems using reconnaissance metadynamics.Adaptive local learning in sampling based motion planning for protein folding.Exploring Valleys without Climbing Every Peak: More Efficient and Forgiving Metabasin Metadynamics via Robust On-the-Fly Bias Domain Restriction.Atomistic theory and simulation of the morphology and structure of ionic nanoparticles.Computational methods for the design of potent aromatase inhibitors.Charting molecular free-energy landscapes with an atlas of collective variables.Mapping transiently formed and sparsely populated conformations on a complex energy landscape.Machine learning estimates of natural product conformational energies.Following easy slope paths on a free energy landscape: the case study of the Trp-cage folding mechanism.Intrinsic map dynamics exploration for uncharted effective free-energy landscapes.Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond.Adaptive enhanced sampling with a path-variable for the simulation of protein folding and aggregation.Focused conformational sampling in proteins.Molecular dynamics based enhanced sampling of collective variables with very large time steps.Some connections between importance sampling and enhanced sampling methods in molecular dynamics.Prediction of binding poses to FXR using multi-targeted docking combined with molecular dynamics and enhanced sampling.Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces.Markov state modeling and dynamical coarse-graining via discrete relaxation path sampling.Well-tempered metadynamics converges asymptotically.Uncertainty in a Markov state model with missing states and rates: Application to a room temperature kinetic model obtained using high temperature molecular dynamics.Heating and flooding: a unified approach for rapid generation of free energy surfaces.Time scale bridging in atomistic simulation of slow dynamics: viscous relaxation and defect activationEnhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-AccelerationSelf assembly and chirality transfer in D-Alaninol on the Cu(100) surfaceNew advances in metadynamics
P2860
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P2860
A self-learning algorithm for biased molecular dynamics.
description
2010 nî lūn-bûn
@nan
2010 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
A self-learning algorithm for biased molecular dynamics.
@ast
A self-learning algorithm for biased molecular dynamics.
@en
A self-learning algorithm for biased molecular dynamics.
@nl
type
label
A self-learning algorithm for biased molecular dynamics.
@ast
A self-learning algorithm for biased molecular dynamics.
@en
A self-learning algorithm for biased molecular dynamics.
@nl
prefLabel
A self-learning algorithm for biased molecular dynamics.
@ast
A self-learning algorithm for biased molecular dynamics.
@en
A self-learning algorithm for biased molecular dynamics.
@nl
P2860
P356
P1476
A self-learning algorithm for biased molecular dynamics.
@en
P2093
Gareth A Tribello
Michele Parrinello
P2860
P304
17509-17514
P356
10.1073/PNAS.1011511107
P407
P577
2010-09-27T00:00:00Z