Machine learned coarse-grained protein force-fields: Are we there yet?

Cooperative multivalent receptor binding promotes exposure of the SARS-CoV-2 fusion machinery core

(2022) Alexander J. Pak et al.   Nature Communications

Bottom-up Coarse-Graining: Principles and Perspectives

(2022) Jaehyeok Jin et al.   Journal of Chemical Theory and Computation

Contrastive Learning of Coarse-Grained Force Fields

(2022) Xinqiang Ding et al.   Journal of Chemical Theory and Computation

The first-principles phase diagram of monolayer nanoconfined water

(2022) Venkat Kapil et al.   NATURE

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

(2022) Lorenzo Gigli et al.   npj Computational Materials

Machine Learning Force Fields

(2021) Oliver T. Unke et al.   CHEMICAL REVIEWS

Physics-Inspired Structural Representations for Molecules and Materials

(2021) Felix Musil et al.   CHEMICAL REVIEWS

Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

(2021) Stephan Thaler et al.   Nature Communications

Machine Learning for Molecular Simulation

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

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

(2020) Jonathan Vandermause et al.   npj Computational Materials

Protein–ligand binding with the coarse-grained Martini model

(2020) Paulo C. T. Souza et al.   Nature Communications

Exploring the landscape of model representations

(2020) Thomas T. Foley et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

(2019) Jiang Wang et al.   ACS Central Science

Understanding Missing Entropy in Coarse-Grained Systems: Addressing Issues of Representability and Transferability

(2019) Jaehyeok Jin et al.   Journal of Physical Chemistry Letters

Less is more: Coarse-grained integrative modeling of large biomolecular assemblies with HADDOCK

(2019) Jorge Roel-Touris et al.   Journal of Chemical Theory and Computation

Coarse-graining auto-encoders for molecular dynamics

(2019) Wujie Wang et al.   npj Computational Materials

Markov State Models: From an Art to a Science

(2018) Brooke E. Husic et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Addressing the temperature transferability of structure based coarse graining models

(2018) David Rosenberger et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Biophysical experiments and biomolecular simulations: A perfect match?

(2018) Sandro Bottaro et al.   SCIENCE

Neural Network Based Prediction of Conformational Free Energies - A New Route toward Coarse-Grained Simulation Models

(2017) Tobias Lemke 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

UCSF ChimeraX: Meeting modern challenges in visualization and analysis

(2017) Thomas D. Goddard et al.   PROTEIN SCIENCE

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

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

Van der Waals Perspective on Coarse-Graining: Progress toward Solving Representability and Transferability Problems

(2016) Nicholas J. H. Dunn et al.   ACCOUNTS OF CHEMICAL RESEARCH

Exploring the aggregation free energy landscape of the amyloid-β protein (1–40)

(2016) Weihua Zheng et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Machine learning scheme for fast extraction of chemically interpretable interatomic potentials

(2016) Pavel E. Dolgirev et al.   AIP Advances

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

A New Multiscale Algorithm and Its Application to Coarse-Grained Peptide Models for Self-Assembly

(2012) Scott P. Carmichael 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

Structure of the yeast F1Fo-ATP synthase dimer and its role in shaping the mitochondrial cristae

(2012) K. M. Davies 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

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

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

Release of content through mechano-sensitive gates in pressurized liposomes

(2010) M. Louhivuori et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Principles of protein folding - A perspective from simple exact models

(2010) Ken A. Dill et al.   PROTEIN SCIENCE

Multiscale Coarse-Graining of the Protein Energy Landscape

(2010) Ronald D. Hills et al.   PLoS Computational Biology

Systematic Multiscale Parameterization of Heterogeneous Elastic Network Models of Proteins

(2008) Edward Lyman et al.   BIOPHYSICAL JOURNAL

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

(2007) Cecilia Clementi   CURRENT OPINION IN STRUCTURAL BIOLOGY