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Machine learning (ML) is transforming all areas of science.
Quantum mechanics of many-electron systems
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Efficient backprop
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New tricks for modelers from the crystallography toolkit: the particle mesh Ewald algorithm and its use in nucleic acid simulations
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Describing protein folding kinetics by molecular dynamics simulations: 1. Theory
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Robust perron cluster analysis in conformation dynamics
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Pattern Recognition and Machine Learning
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Generalized neural-network representation of high-dimensional potential-energy surfaces
J. Behler and M. Parrinello · 2007
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Hierarchical Analysis of Conformational Dynamics in Biomolecules: Transition Networks of Metastable States
F. Noé, I. Horenko, C. Schütte, and J. C. Smith · 2007
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Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics
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Estimation of absolute solvent and solvation shell entropies via permutation reduction
F. Reinhard and H. Grubmüller · 2007
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The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models
W. G. Noid, Jhih-Wei Chu, Gary S. Ayton, Vinod Krishna, Sergei Izvekov, Gregory A. Voth, Avisek Das, and Hans C. Andersen · 2008
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Projection of diffusions on submanifolds: Application to mean force computation
G. Ciccotti, T. Lelièvre, and E. Vanden-Eijnden · 2008
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Coarse-grained models of protein folding: Toy-models or predictive tools?
C. Clementi · 2008
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Coarse Master Equations for Peptide Folding Dynamics
N. V. Buchete and G. Hummer · 2008
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Dynamic mode decomposition of numerical and experimental data
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On relevant dimensions in kernel feature spaces
M. L. Braun, J. M. Buhmann, and K. R. Müller · 2008
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Constructing the full ensemble of folding pathways from short off-equilibrium simulations
F. Noé, C. Schütte, E. Vanden-Eijnden, L. Reich, and T. R. Weikl · 2009
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Extended ensemble approach for deriving transferable coarse-grained potentials
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Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi · 2010
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On the approximation quality of markov state models
M. Sarich, F. Noé, and C. Schütte · 2010
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Density estimation by dual ascent of the log-likelihood
E. G. Tabak and E. Vanden-Eijnden · 2010
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Temporal kernel cca and its application in multimodal neuronal data analysis
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Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments
F. Noé, S. Doose, I. Daidone, M. Löllmann, J. D. Chodera, M. Sauer, and J. C. Smith · 2011
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Markov models of molecular kinetics: Generation and validation
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Exploration of transferability in multiscale coarse-grained peptide models
I. F. Thorpe, D. P. Goldenberg, and G. A. Voth · 2011
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Fast and accurate modeling of molecular atomization energies with machine learning
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. Von Lilienfeld · 2012
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Systematic validation of protein force fields against experimental data
Kresten Lindorff-Larsen, Paul Maragakis, Stefano Piana, Michael P. Eastwood, Ron O. Dror, and David E. Shaw · 2012
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Optimizing solute-water van der waals interactions to reproduce solvation free energies
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Perspective: Coarse-grained models for biomolecular systems
W. G. Noid · 2013
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A variational approach to modeling slow processes in stochastic dynamical systems
F. Noé and F. Nüske · 2013
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Assessment and validation of machine learning methods for predicting molecular atomization energies
K. Hansen, G. Montavon, F. Biegler, S. Fazli, M. Rupp, M. Scheffler, O. Anatole von Lilienfeld, A. Tkatchenko, and K.-R. Müller · 2013
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On representing chemical environments
A. P. Bartók, R. Kondor, and G. Csányi · 2013
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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Identification of slow molecular order parameters for markov model construction
G. Perez-Hernandez, F. Paul, T. Giorgino, G. D Fabritiis, and Frank Noé · 2013
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Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification
S. Röblitz and M. Weber · 2013
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Free energy surface reconstruction from umbrella samples using gaussian process regression
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Variational approach to enhanced sampling and free energy calculations
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On-the-fly learning and sampling of ligand binding by high-throughput molecular simulations
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On dynamic mode decomposition: Theory and applications
J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton, and J. N. Kutz · 2014
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
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Generative adversarial networks
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C. X. Hernández, H. K. Wayment-Steele, M. M. Sultan, B. E. Husic, and V. S. Pande · 2018
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VAMPnets: Deep learning of molecular kinetics
A. Mardt, L. Pasquali, H. Wu, and F. Noé · 2018
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Reweighted autoencoded variational bayes for enhanced sampling (rave)
J. M. L. Ribeiro, P. Bravo, Y. Wang, and P. Tiwary · 2018
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Deep generative markov state models
H. Wu, A. Mardt, L. Pasquali, and F. Noé · 2018
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Kdeep: Protein–ligand absolute binding affinity prediction via 3d-convolutional neural networks
J. Jiménez, M. Skalic, G. Martinez-Rosell, and G. De Fabritiis · 2018
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Ligvoxel: inpainting binding pockets using 3d-convolutional neural networks
M. Skalic, A. Varela-Rial, J. Jiménez, G. Martínez-Rosell, and G. De Fabritiis · 2018
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Deep learning
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FAST conformational searches by balancing exploration/exploitation trade-offs
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Nice: Nonlinear independent components estimation
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SchNet - a deep learning architecture for molecules and materials
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Deep potential: a general representation of a many-body potential energy surface
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Learning free energy landscapes using artificial neural networks
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Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration
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Transferable neural networks for enhanced sampling of protein dynamics
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Adaptive enhanced sampling by force-biasing using neural networks
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A data-driven perspective on the hierarchical assembly of molecular structures
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Variational coarse-graining for molecular dynamics
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Machine learning for molecular dynamics on long timescales
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
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Parallel WaveNet: Fast High-Fidelity Speech Synthesis
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
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Transferable dynamic molecular charge assignment using deep neural networks
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