Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach

Machine Learning for Molecular Simulation

(2020) Frank Noé et al.   Annual Review of Physical Chemistry

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

(2019) Stefan Chmiela et al.   COMPUTER PHYSICS COMMUNICATIONS

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

(2019) Jiang Wang et al.   ACS Central Science

Coarse-graining molecular systems by spectral matching

(2019) Feliks Nüske et al.   JOURNAL OF CHEMICAL PHYSICS

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

(2019) Frank Noé et al.   SCIENCE

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

(2019) K. T. Schütt et al.   Nature Communications

Machine learning for protein folding and dynamics

(2019) Frank Noé et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

Coarse-graining auto-encoders for molecular dynamics

(2019) Wujie Wang et al.   npj Computational Materials

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

(2018) Tristan Bereau et al.   JOURNAL OF CHEMICAL PHYSICS

Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)

(2018) João Marcelo Lamim Ribeiro et al.   JOURNAL OF CHEMICAL PHYSICS

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen et al.   JOURNAL OF CHEMICAL PHYSICS

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

(2018) Christoph Wehmeyer et al.   JOURNAL OF CHEMICAL PHYSICS

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

(2018) Giulio Imbalzano et al.   JOURNAL OF CHEMICAL PHYSICS

DeePCG: Constructing coarse-grained models via deep neural networks

(2018) Linfeng Zhang et al.   JOURNAL OF CHEMICAL PHYSICS

Learning Effective Molecular Models from Experimental Observables

(2018) Justin Chen et al.   Journal of Chemical Theory and Computation

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

VAMPnets for deep learning of molecular kinetics

(2018) Andreas Mardt et al.   Nature Communications

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

A Data-Driven Perspective on the Hierarchical Assembly of Molecular Structures

(2017) Lorenzo Boninsegna et al.   Journal of Chemical Theory and Computation

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

(2017) S. T. John et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling

(2017) Nuria Plattner et al.   Nature Chemistry

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Protein-peptide association kinetics beyond the seconds timescale from atomistic simulations

(2017) Fabian Paul et al.   Nature Communications

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models

(2015) Nuria Plattner et al.   Nature Communications

Fast recovery of free energy landscapes via diffusion-map-directed molecular dynamics

(2014) Jordane Preto et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Activation pathway of Src kinase reveals intermediate states as targets for drug design

(2014) Diwakar Shukla et al.   Nature Communications

Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions

(2013) Mary A. Rohrdanz et al.   Annual Review of Physical Chemistry

Perspective: Coarse-grained models for biomolecular systems

(2013) W. G. Noid   JOURNAL OF CHEMICAL PHYSICS

Identification of slow molecular order parameters for Markov model construction

(2013) Guillermo Pérez-Hernández et al.   JOURNAL OF CHEMICAL PHYSICS

Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9

(2013) Christian R. Schwantes et al.   Journal of Chemical Theory and Computation

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Optimal Number of Coarse-Grained Sites in Different Components of Large Biomolecular Complexes

(2012) Anton V. Sinitskiy et al.   JOURNAL OF PHYSICAL CHEMISTRY B

AWSEM-MD: Protein Structure Prediction Using Coarse-Grained Physical Potentials and Bioinformatically Based Local Structure Biasing

(2012) Aram Davtyan et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Determination of reaction coordinates via locally scaled diffusion map

(2011) Mary A. Rohrdanz et al.   JOURNAL OF CHEMICAL PHYSICS

Pathway and mechanism of drug binding to G-protein-coupled receptors

(2011) Ron O. Dror et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

How Fast-Folding Proteins Fold

(2011) K. Lindorff-Larsen et al.   SCIENCE

Umbrella sampling

(2011) Johannes Kästner   Wiley Interdisciplinary Reviews-Computational Molecular Science

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing

(2010) I. Buch et al.   Journal of Chemical Information and Modeling

Enhanced Modeling via Network Theory: Adaptive Sampling of Markov State Models

(2010) Gregory R. Bowman et al.   Journal of Chemical Theory and Computation

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Ensemble-based classifiers

(2009) Lior Rokach   ARTIFICIAL INTELLIGENCE REVIEW

Effective force coarse-graining

(2009) Yanting Wang et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models

(2008) W. G. Noid et al.   JOURNAL OF CHEMICAL PHYSICS

The relative entropy is fundamental to multiscale and inverse thermodynamic problems

(2008) M. Scott Shell   JOURNAL OF CHEMICAL PHYSICS

Multiscale Simulation of Soft Matter: From Scale Bridging to Adaptive Resolution

(2007) Matej Praprotnik et al.   Annual Review of Physical Chemistry

Coarse-grained models of protein folding: toy models or predictive tools?

(2007) Cecilia Clementi   CURRENT OPINION IN STRUCTURAL BIOLOGY