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Molecular latent space simulators
Hythem Sidky, Wei Chen and Andrew L. Ferguson
Chem. Sci., 2020, 11, 9459
DOI: 10.1039/D0SC03635H

Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems
Pavan Ravindra, Zachary Smith and Pratyush Tiwary
Mol. Syst. Des. Eng., 2020, 5, 339
DOI: 10.1039/C9ME00115H

Rational design of patchy colloidsvialandscape engineering
Andrew W. Long and Andrew L. Ferguson
Mol. Syst. Des. Eng., 2018, 3, 49
DOI: 10.1039/C7ME00077D

Markov Models of Molecular Kinetics
Frank Noé and Edina Rosta
The Journal of Chemical Physics, 2019, 151
DOI: 10.1063/1.5134029

Nonlinear machine learning in simulations of soft and biological materials
J. Wang and A. L. Ferguson
Molecular Simulation, 2018, 44, 1090
DOI: 10.1080/08927022.2017.1400164

Accelerated Simulations of Molecular Systems through Learning of Effective Dynamics
Pantelis R. Vlachas, Julija Zavadlav, Matej Praprotnik and Petros Koumoutsakos
J. Chem. Theory Comput., 2022, 18, 538
DOI: 10.1021/acs.jctc.1c00809

Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design
Wei Chen, Aik Rui Tan and Andrew L. Ferguson
The Journal of Chemical Physics, 2018, 149
DOI: 10.1063/1.5023804

Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution
Md Raisul Kibria, Refo Ilmiya Akbar, Poonam Nidadavolu, Oksana Havryliuk, Sébastien Lafond and Sepinoud Azimi
Sci Rep, 2023, 13
DOI: 10.1038/s41598-023-27729-7

Identification of kinetic order parameters for non-equilibrium dynamics
Fabian Paul, Hao Wu, Maximilian Vossel, Bert L. de Groot and Frank Noé
The Journal of Chemical Physics, 2019, 150
DOI: 10.1063/1.5083627

FSATOOL: A useful tool to do the conformational sampling and trajectory analysis work for biomolecules
Haomiao Zhang, Qiankun Gong, Haozhe Zhang and Changjun Chen
J Comput Chem, 2020, 41, 156
DOI: 10.1002/jcc.26083

Artificial neural networks for predicting charge transfer coupling
Chun-I Wang, Ignasius Joanito, Chang-Feng Lan and Chao-Ping Hsu
The Journal of Chemical Physics, 2020, 153
DOI: 10.1063/5.0023697

Commute Maps: Separating Slowly Mixing Molecular Configurations for Kinetic Modeling
Frank Noé, Ralf Banisch and Cecilia Clementi
J. Chem. Theory Comput., 2016, 12, 5620
DOI: 10.1021/acs.jctc.6b00762

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles
Sebastian Stolzenberg and Alfonso Valencia
Bioinformatics, 2019, 35, 1588
DOI: 10.1093/bioinformatics/bty818

ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis
Ali Hashemi, Sana Bougueroua, Marie-Pierre Gaigeot and Evgeny A. Pidko
J. Chem. Theory Comput., 2022, 18, 7470
DOI: 10.1021/acs.jctc.2c00404

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems
Paraskevi Gkeka, Gabriel Stoltz, Amir Barati Farimani, Zineb Belkacemi, Michele Ceriotti, John D. Chodera, Aaron R. Dinner, Andrew L. Ferguson, Jean-Bernard Maillet, Hervé Minoux, Christine Peter, Fabio Pietrucci, Ana Silveira, Alexandre Tkatchenko, Zofia Trstanova, Rafal Wiewiora and Tony Lelièvre
J. Chem. Theory Comput., 2020, 16, 4757
DOI: 10.1021/acs.jctc.0c00355

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Christoph Wehmeyer and Frank Noé
The Journal of Chemical Physics, 2018, 148
DOI: 10.1063/1.5011399

Collective variables for the study of long-time kinetics from molecular trajectories: theory and methods
Frank Noé and Cecilia Clementi
Current Opinion in Structural Biology, 2017, 43, 141
DOI: 10.1016/j.sbi.2017.02.006

Collective variable-based enhanced sampling and machine learning
Ming Chen
Eur. Phys. J. B, 2021, 94
DOI: 10.1140/epjb/s10051-021-00220-w

Enhancing pairwise state-transition weights: A new weighting scheme in simulated tempering that can minimize transition time between a pair of conformational states
Qin Qiao, Hou-Dao Zhang and Xuhui Huang
The Journal of Chemical Physics, 2016, 144
DOI: 10.1063/1.4946793

pyCHARMM: Embedding CHARMM Functionality in a Python Framework
Joshua Buckner, Xiaorong Liu, Arghya Chakravorty, Yujin Wu, Luis F. Cervantes, Thanh T. Lai and Charles L. Brooks
J. Chem. Theory Comput., 2023, 19, 3752
DOI: 10.1021/acs.jctc.3c00364

Computational methods for exploring protein conformations
Jane R. Allison
Biochemical Society Transactions, 2020, 48, 1707
DOI: 10.1042/BST20200193

Unsupervised machine learning in atomistic simulations, between predictions and understanding
Michele Ceriotti
The Journal of Chemical Physics, 2019, 150
DOI: 10.1063/1.5091842

MIST: A simple and efficient molecular dynamics abstraction library for integrator development
Iain Bethune, Ralf Banisch, Elena Breitmoser, Antonia B.K. Collis, Gordon Gibb, Gianpaolo Gobbo, Charles Matthews, Graeme J. Ackland and Benedict J. Leimkuhler
Computer Physics Communications, 2019, 236, 224
DOI: 10.1016/j.cpc.2018.10.006

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output
Hanna Baltrukevich and Sabina Podlewska
Front. Pharmacol., 2022, 13
DOI: 10.3389/fphar.2022.844293

KIF—Key Interactions Finder: A program to identify the key molecular interactions that regulate protein conformational changes
Rory M. Crean, Joanna S. G. Slusky, Peter M. Kasson and Shina Caroline Lynn Kamerlin
The Journal of Chemical Physics, 2023, 158
DOI: 10.1063/5.0140882

Local and global perspectives on diffusion maps in the analysis of molecular systems
Z. Trstanova, B. Leimkuhler and T. Lelièvre
Proc. R. Soc. A., 2020, 476, 20190036
DOI: 10.1098/rspa.2019.0036

Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables
Kirill Shmilovich and Andrew L. Ferguson
J. Phys. Chem. A, 2023, 127, 3497
DOI: 10.1021/acs.jpca.3c00505

Markov state modeling reveals alternative unbinding pathways for peptide–MHC complexes
Jayvee R. Abella, Dinler Antunes, Kyle Jackson, Gregory Lizée, Cecilia Clementi and Lydia E. Kavraki
Proc. Natl. Acad. Sci. U.S.A., 2020, 117, 30610
DOI: 10.1073/pnas.2007246117

Path-sampling strategies for simulating rare events in biomolecular systems
Lillian T Chong, Ali S Saglam and Daniel M Zuckerman
Current Opinion in Structural Biology, 2017, 43, 88
DOI: 10.1016/j.sbi.2016.11.019

Synapse: Synthetic application profiler and emulator
Andre Merzky, Ming Tai Ha, Matteo Turilli and Shantenu Jha
Journal of Computational Science, 2018, 27, 329
DOI: 10.1016/j.jocs.2018.06.012

Extensible and Scalable Adaptive Sampling on Supercomputers
Eugen Hruska, Vivekanandan Balasubramanian, Hyungro Lee, Shantenu Jha and Cecilia Clementi
J. Chem. Theory Comput., 2020, 16, 7915
DOI: 10.1021/acs.jctc.0c00991

Protein Structure Validation Derives a Smart Conformational Search in a Physically Relevant Configurational Subspace
Takunori Yasuda, Rikuri Morita, Yasuteru Shigeta and Ryuhei Harada
J. Chem. Inf. Model., 2022, 62, 6217
DOI: 10.1021/acs.jcim.2c01173

Weighted Ensemble Simulation: Review of Methodology, Applications, and Software
Daniel M. Zuckerman and Lillian T. Chong
Annu. Rev. Biophys., 2017, 46, 43
DOI: 10.1146/annurev-biophys-070816-033834

The RED scheme: Rate-constant estimation from pre-steady state weighted ensemble simulations
Alex J. DeGrave, Anthony T. Bogetti and Lillian T. Chong
The Journal of Chemical Physics, 2021, 154
DOI: 10.1063/5.0041278

Quantitative comparison of adaptive sampling methods for protein dynamics
Eugen Hruska, Jayvee R. Abella, Feliks Nüske, Lydia E. Kavraki and Cecilia Clementi
The Journal of Chemical Physics, 2018, 149
DOI: 10.1063/1.5053582

Investigating Molecular Kinetics by Variationally Optimized Diffusion Maps
Lorenzo Boninsegna, Gianpaolo Gobbo, Frank Noé and Cecilia Clementi
J. Chem. Theory Comput., 2015, 11, 5947
DOI: 10.1021/acs.jctc.5b00749

Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach
Jiang Wang, Stefan Chmiela, Klaus-Robert Müller, Frank Noé and Cecilia Clementi
The Journal of Chemical Physics, 2020, 152
DOI: 10.1063/5.0007276

Kinetic Distance and Kinetic Maps from Molecular Dynamics Simulation
Frank Noé and Cecilia Clementi
J. Chem. Theory Comput., 2015, 11, 5002
DOI: 10.1021/acs.jctc.5b00553

Design and Performance Characterization of RADICAL-Pilot on Leadership-Class Platforms
Andre Merzky, Matteo Turilli, Mikhail Titov, Aymen Al-Saadi and Shantenu Jha
IEEE Trans. Parallel Distrib. Syst., 2022, 33, 818
DOI: 10.1109/TPDS.2021.3105994

Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias
Feliks Nüske, Hao Wu, Jan-Hendrik Prinz, Christoph Wehmeyer, Cecilia Clementi and Frank Noé
The Journal of Chemical Physics, 2017, 146
DOI: 10.1063/1.4976518

Multiagent Reinforcement Learning-Based Adaptive Sampling for Conformational Dynamics of Proteins
Diego E. Kleiman and Diwakar Shukla
J. Chem. Theory Comput., 2022, 18, 5422
DOI: 10.1021/acs.jctc.2c00683

Molecular enhanced sampling with autoencoders: On‐the‐fly collective variable discovery and accelerated free energy landscape exploration
Wei Chen and Andrew L. Ferguson
J Comput Chem, 2018, 39, 2079
DOI: 10.1002/jcc.25520

How Effectively Can Adaptive Sampling Methods Capture Spontaneous Ligand Binding?
Robin M. Betz and Ron O. Dror
J. Chem. Theory Comput., 2019, 15, 2053
DOI: 10.1021/acs.jctc.8b00913

Free-energy landscape of ion-channel voltage-sensor–domain activation
Lucie Delemotte, Marina A. Kasimova, Michael L. Klein, Mounir Tarek and Vincenzo Carnevale
Proc. Natl. Acad. Sci. U.S.A., 2015, 112, 124
DOI: 10.1073/pnas.1416959112

Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models
Nuria Plattner and Frank Noé
Nat Commun, 2015, 6
DOI: 10.1038/ncomms8653

Collective mode Brownian dynamics: A method for fast relaxation of statistical ensembles
Kevin S. Silmore and James W. Swan
The Journal of Chemical Physics, 2020, 152
DOI: 10.1063/1.5129648

Machine learning approaches for analyzing and enhancing molecular dynamics simulations
Yihang Wang, João Marcelo Lamim Ribeiro and Pratyush Tiwary
Current Opinion in Structural Biology, 2020, 61, 139
DOI: 10.1016/j.sbi.2019.12.016

CoCo-MD: A Simple and Effective Method for the Enhanced Sampling of Conformational Space
Ardita Shkurti, Ioanna Danai Styliari, Vivek Balasubramanian, Iain Bethune, Conrado Pedebos, Shantenu Jha and Charles A. Laughton
J. Chem. Theory Comput., 2019, 15, 2587
DOI: 10.1021/acs.jctc.8b00657

Landmark diffusion maps (L-dMaps): Accelerated manifold learning out-of-sample extension
Andrew W. Long and Andrew L. Ferguson
Applied and Computational Harmonic Analysis, 2019, 47, 190
DOI: 10.1016/j.acha.2017.08.004

Dynamic graphical models of molecular kinetics
Simon Olsson and Frank Noé
Proc. Natl. Acad. Sci. U.S.A., 2019, 116, 15001
DOI: 10.1073/pnas.1901692116

Unsupervised Learning Methods for Molecular Simulation Data
Aldo Glielmo, Brooke E. Husic, Alex Rodriguez, Cecilia Clementi, Frank Noé and Alessandro Laio
Chem. Rev., 2021, 121, 9722
DOI: 10.1021/acs.chemrev.0c01195

Non-Markov-Type Analysis and Diffusion Map Analysis for Molecular Dynamics Trajectory of Chignolin at a High Temperature
Hiroshi Fujisaki, Hiromichi Suetani, Luca Maragliano and Ayori Mitsutake
Life, 2022, 12, 1188
DOI: 10.3390/life12081188

Exploration of Chemical Compound, Conformer, and Reaction Space with Meta-Dynamics Simulations Based on Tight-Binding Quantum Chemical Calculations
Stefan Grimme
J. Chem. Theory Comput., 2019, 15, 2847
DOI: 10.1021/acs.jctc.9b00143

Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics
Zachary Smith, Pavan Ravindra, Yihang Wang, Rory Cooley and Pratyush Tiwary
J. Phys. Chem. B, 2020, 124, 8221
DOI: 10.1021/acs.jpcb.0c03985

Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling
Nuria Plattner, Stefan Doerr, Gianni De Fabritiis and Frank Noé
Nature Chem, 2017, 9, 1005
DOI: 10.1038/nchem.2785

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes
Dinler A. Antunes, Jayvee R. Abella, Didier Devaurs, Maurício M. Rigo and Lydia E. Kavraki
CTMC, 2019, 18, 2239
DOI: 10.2174/1568026619666181224101744

Multi-body effects in a coarse-grained protein force field
Jiang Wang, Nicholas Charron, Brooke Husic, Simon Olsson, Frank Noé and Cecilia Clementi
The Journal of Chemical Physics, 2021, 154
DOI: 10.1063/5.0041022

Simulations Reveal Multiple Intermediates in the Unzipping Mechanism of Neuronal SNARE Complex
Giovanni Pinamonti, Gregory Campo, Justin Chen, Alex Kluber and Cecilia Clementi
Biophysical Journal, 2018, 115, 1470
DOI: 10.1016/j.bpj.2018.08.043

Perspective on integrating machine learning into computational chemistry and materials science
Julia Westermayr, Michael Gastegger, Kristof T. Schütt and Reinhard J. Maurer
The Journal of Chemical Physics, 2021, 154
DOI: 10.1063/5.0047760

OpenPathSampling: A Python Framework for Path Sampling Simulations. 1. Basics
David W. H. Swenson, Jan-Hendrik Prinz, Frank Noe, John D. Chodera and Peter G. Bolhuis
J. Chem. Theory Comput., 2019, 15, 813
DOI: 10.1021/acs.jctc.8b00626

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
Hythem Sidky, Wei Chen and Andrew L. Ferguson
Molecular Physics, 2020, 118
DOI: 10.1080/00268976.2020.1737742

Data-Driven Molecular Dynamics: A Multifaceted Challenge
Mattia Bernetti, Martina Bertazzo and Matteo Masetti
Pharmaceuticals, 2020, 13, 253
DOI: 10.3390/ph13090253

Using Dimensionality Reduction to Systematically Expand Conformational Sampling of Intrinsically Disordered Peptides
Oleksandra Kukharenko, Kevin Sawade, Jakob Steuer and Christine Peter
J. Chem. Theory Comput., 2016, 12, 4726
DOI: 10.1021/acs.jctc.6b00503

Reinforcement Learning Based Adaptive Sampling: REAPing Rewards by Exploring Protein Conformational Landscapes
Zahra Shamsi, Kevin J. Cheng and Diwakar Shukla
J. Phys. Chem. B, 2018, 122, 8386
DOI: 10.1021/acs.jpcb.8b06521

Machine learning for protein folding and dynamics
Frank Noé, Gianni De Fabritiis and Cecilia Clementi
Current Opinion in Structural Biology, 2020, 60, 77
DOI: 10.1016/j.sbi.2019.12.005

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics
Tatiana Maximova, Ryan Moffatt, Buyong Ma, Ruth Nussinov, Amarda Shehu and Bert L. de Groot
PLoS Comput Biol, 2016, 12, e1004619
DOI: 10.1371/journal.pcbi.1004619

Active Learning of the Conformational Ensemble of Proteins Using Maximum Entropy VAMPNets
Diego E. Kleiman and Diwakar Shukla
J. Chem. Theory Comput., 2023, 19, 4377
DOI: 10.1021/acs.jctc.3c00040

APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations
Jayvee Abella, Dinler Antunes, Cecilia Clementi and Lydia Kavraki
Molecules, 2019, 24, 881
DOI: 10.3390/molecules24050881

Toward Automated Sampling of Polymorph Nucleation and Free Energies with the SGOOP and Metadynamics
Ziyue Zou, Sun-Ting Tsai and Pratyush Tiwary
J. Phys. Chem. B, 2021, 125, 13049
DOI: 10.1021/acs.jpcb.1c07595

Markov field models: Scaling molecular kinetics approaches to large molecular machines
Tim Hempel, Simon Olsson and Frank Noé
Current Opinion in Structural Biology, 2022, 77, 102458
DOI: 10.1016/j.sbi.2022.102458

The mechanism of RNA base fraying: Molecular dynamics simulations analyzed with core-set Markov state models
Giovanni Pinamonti, Fabian Paul, Frank Noé, Alex Rodriguez and Giovanni Bussi
The Journal of Chemical Physics, 2019, 150
DOI: 10.1063/1.5083227

Machine learning and data science in soft materials engineering
Andrew L Ferguson
J. Phys.: Condens. Matter, 2018, 30, 043002
DOI: 10.1088/1361-648X/aa98bd

Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks
Ferry Hooft, Alberto Pérez de Alba Ortíz and Bernd Ensing
J. Chem. Theory Comput., 2021, 17, 2294
DOI: 10.1021/acs.jctc.0c00981

Protein-peptide association kinetics beyond the seconds timescale from atomistic simulations
Fabian Paul, Christoph Wehmeyer, Esam T. Abualrous, Hao Wu, Michael D. Crabtree, Johannes Schöneberg, Jane Clarke, Christian Freund, Thomas R. Weikl and Frank Noé
Nat Commun, 2017, 8
DOI: 10.1038/s41467-017-01163-6

Deep learning collective variables from transition path ensemble
Dhiman Ray, Enrico Trizio and Michele Parrinello
The Journal of Chemical Physics, 2023, 158
DOI: 10.1063/5.0148872

Recruiting machine learning methods for molecular simulations of proteins
Shriyaa Mittal and Diwakar Shukla
Molecular Simulation, 2018, 44, 891
DOI: 10.1080/08927022.2018.1448976

HTMD: High-Throughput Molecular Dynamics for Molecular Discovery
S. Doerr, M. J. Harvey, Frank Noé and G. De Fabritiis
J. Chem. Theory Comput., 2016, 12, 1845
DOI: 10.1021/acs.jctc.6b00049

Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets
Wei Chen, Hythem Sidky and Andrew L. Ferguson
The Journal of Chemical Physics, 2019, 150
DOI: 10.1063/1.5092521

The intrinsically disordered N-terminus of the voltage-dependent anion channel
Jordane Preto, Isabelle Krimm and Alexander MacKerell
PLoS Comput Biol, 2021, 17, e1008750
DOI: 10.1371/journal.pcbi.1008750

Using Diffusion Maps to Analyze Reaction Dynamics for a Hydrogen Combustion Benchmark Dataset
Taehee Ko, Joseph P. Heindel, Xingyi Guan, Teresa Head-Gordon, David B. Williams-Young and Chao Yang
J. Chem. Theory Comput., 2023, 19, 5872
DOI: 10.1021/acs.jctc.3c00426