Vijay S. Pande

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All published works

Action	Title	Year	Authors
+ PDF Chat	OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials	2023	Peter Eastman Raimondas Galvelis Raúl P. Peláez Charlles R. A. Abreu Stephen E. Farr Emilio Gallicchio Anton Gorenko Michael M. Henry Frank Hu Jing Huang
+ PDF Chat	Folding@home: Achievements from over 20 years of citizen science herald the exascale era	2023	Vincent A. Voelz Vijay S. Pande Gregory R. Bowman
+	Folding@home: achievements from over twenty years of citizen science herald the exascale era	2023	Vincent A. Voelz Vijay S. Pande Gregory R. Bowman
+	OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials	2023	Peter Eastman Raimondas Galvelis Raúl P. Peláez Charlles R. A. Abreu Stephen E. Farr Emilio Gallicchio Anton Gorenko Michael M. Henry Frank Hu Jing Huang
+	Classical Quantum Optimization with Neural Network Quantum States.	2019	Joseph Gomes Keri A. McKiernan Peter Eastman Vijay S. Pande
+	Physical machine learning outperforms "human learning" in Quantum Chemistry	2019	Anton V. Sinitskiy Vijay S. Pande
+	Predicting Gene Expression Between Species with Neural Networks	2019	Peter Eastman Vijay S. Pande
+	Pre-training Graph Neural Networks.	2019	Weihua Hu Bowen Liu Joseph Gomes Marinka Žitnik Percy Liang Vijay S. Pande Jure Leskovec
+	Predicting Toxicity from Gene Expression with Neural Networks	2019	Peter Eastman Vijay S. Pande
+	Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization	2019	Evan N. Feinberg Robert P. Sheridan Elizabeth Joshi Vijay S. Pande Alan C. Cheng
+	Strategies for Pre-training Graph Neural Networks	2019	Weihua Hu Bowen Liu Joseph Gomes Marinka Žitnik Percy Liang Vijay S. Pande Jure Leskovec
+	Physical machine learning outperforms "human learning" in Quantum Chemistry	2019	Anton V. Sinitskiy Vijay S. Pande
+	Predicting Gene Expression Between Species with Neural Networks	2019	Peter Eastman Vijay S. Pande
+	Predicting Toxicity from Gene Expression with Neural Networks	2019	Peter Eastman Vijay S. Pande
+ PDF Chat	Note: Variational encoding of protein dynamics benefits from maximizing latent autocorrelation	2018	Hannah K. Wayment-Steele Vijay S. Pande
+	PotentialNet for Molecular Property Prediction	2018	Evan N. Feinberg Debnil Sur Zhenqin Wu Brooke E. Husic Huanghao Mai Yang Li Saisai Sun Jianyi Yang Bharath Ramsundar Vijay S. Pande
+ PDF Chat	Automated design of collective variables using supervised machine learning	2018	Mohammad M. Sultan Vijay S. Pande
+	Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss	2018	Rishi Sharma Amir Barati Farimani Joe Gomes Peter Eastman Vijay S. Pande
+ PDF Chat	Variational encoding of complex dynamics	2018	Carlos X. Hernández Hannah K. Wayment-Steele Mohammad M. Sultan Brooke E. Husic Vijay S. Pande
+	Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation	2018	Jiaxuan You Bowen Liu Rex Ying Vijay S. Pande Jure Leskovec
+ PDF Chat	Communication: Adaptive boundaries in multiscale simulations	2018	Jason A. Wagoner Vijay S. Pande
+	Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation	2018	Hannah K. Wayment-Steele Vijay S. Pande
+	Machine Learning Harnesses Molecular Dynamics to Discover New $\mu$ Opioid Chemotypes	2018	Evan N. Feinberg Amir Barati Farimani Rajendra Uprety Amanda Hunkele Gavril W. Pasternak Susruta Majumdar Vijay S. Pande
+	Spatial Graph Convolutions for Drug Discovery	2018	Evan N. Feinberg Debnil Sur Brooke E. Husic Doris Mai Yang Li Jianyi Yang Bharath Ramsundar Vijay S. Pande
+	PotentialNet for Molecular Property Prediction	2018	Evan N. Feinberg Debnil Sur Zhenqin Wu Brooke E. Husic Huanghao Mai Yang Li Saisai Sun Jianyi Yang Bharath Ramsundar Vijay S. Pande
+ PDF Chat	Transferable Neural Networks for Enhanced Sampling of Protein Dynamics	2018	Mohammad M. Sultan Hannah K. Wayment-Steele Vijay S. Pande
+	Decision functions from supervised machine learning algorithms as collective variables for accelerating molecular simulations.	2018	Mohammad M. Sultan Vijay S. Pande
+	Automated design of collective variables using supervised machine learning.	2018	Mohammad M. Sultan Vijay S. Pande
+ PDF Chat	Binding Pathway of Opiates to μ-Opioid Receptors Revealed by Machine Learning	2018	Amir Barati Farimani Evan N. Feinberg Vijay S. Pande
+ PDF Chat	Theoretical restrictions on longest implicit time scales in Markov state models of biomolecular dynamics	2018	Anton V. Sinitskiy Vijay S. Pande
+	Transferable neural networks for enhanced sampling of protein dynamics	2018	Mohammad M. Sultan Hannah K. Wayment-Steele Vijay S. Pande
+	Using Deep Learning for Segmentation and Counting within Microscopy Data	2018	Carlos X. Hernández Mohammad M. Sultan Vijay S. Pande
+	SentRNA: Improving computational RNA design by incorporating a prior of human design strategies	2018	Jade Shi Rhiju Das Vijay S. Pande
+	Deep Learning Phase Segregation	2018	Amir Barati Farimani Joseph Gomes Rishi Sharma Franklin L. Lee Vijay S. Pande
+	Improved Training with Curriculum GANs	2018	Rishi Sharma Shane Barratt Stefano Ermon Vijay S. Pande
+	Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)	2018	Anton V. Sinitskiy Vijay S. Pande
+	Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation	2018	Jiaxuan You Bowen Liu Rex Ying Vijay S. Pande Jure Leskovec
+	Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss	2018	Rishi Sharma Amir Barati Farimani Joe Gomes Peter Eastman Vijay S. Pande
+	Machine Learning Harnesses Molecular Dynamics to Discover New $μ$ Opioid Chemotypes	2018	Evan N. Feinberg Amir Barati Farimani Rajendra Uprety Amanda Hunkele Gavril W. Pasternak Susruta Majumdar Vijay S. Pande
+	PotentialNet for Molecular Property Prediction	2018	Evan N. Feinberg Debnil Sur Zhenqin Wu Brooke E. Husic Huanghao Mai Yang Li Saisai Sun Jianyi Yang Bharath Ramsundar Vijay S. Pande
+	Transferable neural networks for enhanced sampling of protein dynamics	2018	Mohammad M. Sultan Hannah K. Wayment-Steele Vijay S. Pande
+	Automated design of collective variables using supervised machine learning	2018	Mohammad M. Sultan Vijay S. Pande
+ PDF Chat	Note: MSM lag time cannot be used for variational model selection	2017	Brooke E. Husic Vijay S. Pande
+ PDF Chat	MoleculeNet: a benchmark for molecular machine learning	2017	Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes Caleb Geniesse Aneesh Pappu Karl Leswing Vijay S. Pande
+	Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models	2017	Bowen Liu Bharath Ramsundar Prasad Kawthekar Jade Shi Joseph Gomes Quang Luu Nguyen Stephen Ho Jack L. Sloane Paul A. Wender Vijay S. Pande
+ PDF Chat	Computationally Discovered Potentiating Role of Glycans on NMDA Receptors	2017	Anton V. Sinitskiy Nathaniel Stanley David H. Hackos Jesse E. Hanson Benjamin D. Sellers Vijay S. Pande
+	Low Data Drug Discovery with One-Shot Learning	2017	Han Altae-Tran Bharath Ramsundar Aneesh Pappu Vijay S. Pande
+	Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity	2017	Joseph Gomes Bharath Ramsundar Evan N. Feinberg Vijay S. Pande
+ PDF Chat	Identification of simple reaction coordinates from complex dynamics	2017	Robert T. McGibbon Brooke E. Husic Vijay S. Pande
+	Deep Learning the Physics of Transport Phenomena	2017	Amir Barati Farimani Joseph Gomes Vijay S. Pande
+	Unsupervised learning of dynamical and molecular similarity using variance minimization	2017	Brooke E. Husic Vijay S. Pande
+	MoleculeNet: A Benchmark for Molecular Machine Learning	2017	Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes Caleb Geniesse Aneesh Pappu Karl Leswing Vijay S. Pande
+	Retrosynthetic reaction prediction using neural sequence-to-sequence models	2017	Bowen Liu Bharath Ramsundar Prasad Kawthekar Jade Shi Joseph Gomes Quang Luu Nguyen Stephen Ho Jack L. Sloane Paul A. Wender Vijay S. Pande
+	Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity	2017	J. Anthony Gomes Bharath Ramsundar Evan N. Feinberg Vijay S. Pande
+	Computationally Discovered Potentiating Role of Glycans on NMDA Receptors	2016	Anton V. Sinitskiy Nathaniel Stanley David H. Hackos Jesse E. Hanson Benjamin D. Sellers Vijay S. Pande
+	Learning Protein Dynamics with Metastable Switching Systems.	2016	Bharath Ramsundar Vijay S. Pande
+ PDF Chat	Molecular graph convolutions: moving beyond fingerprints	2016	Steven Kearnes Kevin McCloskey Marc Berndl Vijay S. Pande Patrick Riley
+ PDF Chat	ROCS-derived features for virtual screening	2016	Steven Kearnes Vijay S. Pande
+	Modeling Industrial ADMET Data with Multitask Networks	2016	Steven Kearnes Brian Goldman Vijay S. Pande
+	Low Data Drug Discovery with One-shot Learning	2016	Han Altae-Tran Bharath Ramsundar Aneesh Pappu Vijay S. Pande
+	Modeling Industrial ADMET Data with Multitask Networks	2016	Steven Kearnes B Goldman Vijay S. Pande
+	Learning Protein Dynamics with Metastable Switching Systems	2016	Bharath Ramsundar Vijay S. Pande
+	Computationally Discovered Potentiating Role of Glycans on NMDA Receptors	2016	Anton V. Sinitskiy Nathaniel Stanley David H. Hackos Jesse E. Hanson Benjamin D. Sellers Vijay S. Pande
+ PDF Chat	Efficient maximum likelihood parameterization of continuous-time Markov processes	2015	Robert T. McGibbon Vijay S. Pande
+ PDF Chat	Percolation-like phase transitions in network models of protein dynamics	2015	Jeffrey K. Weber Vijay S. Pande
+	Efficient maximum likelihood parameterization of continuous-time Markov processes	2015	Robert T. McGibbon Vijay S. Pande
+ PDF Chat	Variational cross-validation of slow dynamical modes in molecular kinetics	2015	Robert T. McGibbon Vijay S. Pande
+	Massively Multitask Networks for Drug Discovery	2015	Bharath Ramsundar Steven Kearnes Patrick Riley Dale R. Webster David E. Konerding Vijay S. Pande
+	Efficient maximum likelihood parameterization of continuous-time Markov processes	2015	Robert T. McGibbon Vijay S. Pande
+ PDF Chat	Perspective: Markov models for long-timescale biomolecular dynamics	2014	Christian R. Schwantes Robert T. McGibbon Vijay S. Pande
+	Efficient inference of protein structural ensembles	2014	Thomas J. Lane Christian R. Schwantes Kyle A. Beauchamp Vijay S. Pande
+	Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models	2014	Robert T. McGibbon Bharath Ramsundar Mohammad M. Sultan Gert Kiss Vijay S. Pande
+ PDF Chat	Inferring the Rate-Length Law of Protein Folding	2013	Thomas J. Lane Vijay S. Pande
+ PDF Chat	Probing the origins of two-state folding	2013	Thomas J. Lane Christian R. Schwantes Kyle A. Beauchamp Vijay S. Pande
+ PDF Chat	Inclusion of persistence length-based secondary structure in replica field theoretic models of heteropolymer freezing	2013	Jeffrey K. Weber Vijay S. Pande
+ PDF Chat	Eigenvalues of the homogeneous finite linear one step master equation: Applications to downhill folding	2012	Thomas J. Lane Vijay S. Pande
+ PDF Chat	Reducing the effect of Metropolization on mixing times in molecular dynamics simulations	2012	Jason A. Wagoner Vijay S. Pande
+ PDF Chat	Splitting Probabilities as a Test of Reaction Coordinate Choice in Single-Molecule Experiments	2011	John D. Chodera Vijay S. Pande
+ PDF Chat	Rationally Designed Turn Promoting Mutation in the Amyloid-β Peptide Sequence Stabilizes Oligomers in Solution	2011	Jayakumar Rajadas Corey W. Liu Paul Novick Nicholas Kelley Mohammed Inayathullah Melburne C. LeMieux Vijay S. Pande
+	A robust approach to estimating rates from time-correlation functions	2011	John D. Chodera Phillip Elms William C. Swope Jan-Hendrik Prinz Susan Marqusee Carlos Bustamante Frank Noé Vijay S. Pande
+ PDF Chat	Simple Theory of Protein Folding Kinetics	2010	Vijay S. Pande
+ PDF Chat	Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU	2010	Imran S. Haque Vijay S. Pande
+	Bayesian Detection of Intensity Changes in Single Molecule and Molecular Dynamics Trajectories	2009	Daniel L. Ensign Vijay S. Pande
+ PDF Chat	Topological methods for exploring low-density states in biomolecular folding pathways	2009	Yuan Yao Jian Sun Xuhui Huang Gregory R. Bowman Satwinder Singh Michael Lesnick Leonidas Guibas Vijay S. Pande Gunnar Carlsson
+	Folding@Home and Genome@Home: Using distributed computing to tackle previously intractable problems in computational biology	2009	Stefan Larson Michael R. Shirts Vijay S. Pande Christopher D. Snow
+	Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU	2009	Imran S. Haque Vijay S. Pande
+ PDF Chat	Potential for Modulation of the Hydrophobic Effect Inside Chaperonins	2008	Jeremy L. England Vijay S. Pande
+	N-Body Simulations on GPUs	2007	Erich Elsen Vishal Vaidyanathan Mike Houston Vijay S. Pande Pat Hanrahan Eric Darve
+	Bayesian update method for adaptive weighted sampling	2006	Sanghyun Park Daniel L. Ensign Vijay S. Pande
+	Folding probabilities: A novel approach to folding transitions and the two-dimensional Ising-model	2004	Peter Lenz Bojan Žagrović Jessica Shapiro Vijay S. Pande
+ PDF Chat	Freezing of compact random heteropolymers with correlated sequence fluctuations	1998	Arup K. Chakraborty Eugene I. Shakhnovich Vijay S. Pande
+ PDF Chat	Freezing Transition of Compact Polyampholytes	1996	Vijay S. Pande Alexander Y. Grosberg Chris Joerg Mehran Kardar Toyoichi Tanaka
+ PDF Chat	Is Heteropolymer Freezing Well Described by the Random Energy Model?	1996	Vijay S. Pande Alexander Y. Grosberg Chris Joerg Toyoichi Tanaka
+ PDF Chat	How accurate must potentials be for successful modeling of protein folding?	1995	Vijay S. Pande Alexander Y. Grosberg Toyoichi Tanaka
+ PDF Chat	Freezing transition of random heteropolymers consisting of an arbitrary set of monomers	1995	Vijay S. Pande Alexander Y. Grosberg Toyoichi Tanaka
+	Phase diagram of imprinted copolymers	1994	Vijay S. Pande Alexander Y. Grosberg Toyoichi Tanaka
+	Enumerations of the Hamiltonian walks on a cubic sublattice	1994	Vijay S. Pande Chris Joerg Alexander Y. Grosberg Toyoichi Tanaka

Common Coauthors

Coauthor	Papers Together
Bharath Ramsundar	16
Evan N. Feinberg	12
Mohammad M. Sultan	10
Joseph Gomes	10
Peter Eastman	9
Brooke E. Husic	8
Amir Barati Farimani	7
Anton V. Sinitskiy	7
Robert T. McGibbon	7
Toyoichi Tanaka	6
Hannah K. Wayment-Steele	6
Alexander Y. Grosberg	6
Zhenqin Wu	5
Steven Kearnes	5
Debnil Sur	4
Rishi Sharma	4
Thomas J. Lane	4
Jure Leskovec	4
John D. Chodera	4
Aneesh Pappu	4
Saisai Sun	3
David H. Hackos	3
Christian R. Schwantes	3
Chris Joerg	3
Jade Shi	3
Jesse E. Hanson	3
Nathaniel Stanley	3
Gregory R. Bowman	3
Bowen Liu	3
Yang Li	3
Jianyi Yang	3
Benjamin D. Sellers	3
Raimondas Galvelis	2
Raúl P. Peláez	2
Imran S. Haque	2
Amanda Hunkele	2
Jiaxuan You	2
Paul A. Wender	2
Joe Gomes	2
Julien Michel	2
Quang Luu Nguyen	2
Ivy Zhang	2
Patrick Riley	2
Jason M. Swails	2
Han Altae-Tran	2
Daniel L. Ensign	2
M. Henry	2
Carlos X. Hernández	2
Gianni De Fabritiis	2
Andrew C. Simmonett	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Identification of slow molecular order parameters for Markov model construction	2013	Guillermo Pérez-Hernández Fabian Paul Toni Giorgino Gianni De Fabritiis Frank Noé	9
+ PDF Chat	Variational cross-validation of slow dynamical modes in molecular kinetics	2015	Robert T. McGibbon Vijay S. Pande	7
+	Scikit-learn: Machine Learning in Python	2012	Fabián Pedregosa Gaël Varoquaux Alexandre Gramfort Vincent Michel Bertrand Thirion Olivier Grisel Mathieu Blondel Peter Prettenhofer Ron J. Weiss Vincent Dubourg	7
+ PDF Chat	Quantum-chemical insights from deep tensor neural networks	2017	Kristof T. Schütt Farhad Arbabzadah Stefan Chmiela K. Müller Alexandre Tkatchenko	7
+	Massively Multitask Networks for Drug Discovery	2015	Bharath Ramsundar Steven Kearnes Patrick Riley Dale R. Webster David E. Konerding Vijay S. Pande	6
+ PDF Chat	A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems	2013	Frank Noé Feliks Nüske	6
+	Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules	2018	Rafael Gómez‐Bombarelli Jennifer N. Wei David Duvenaud José Miguel Hernández-Lobato Benjamín Sánchez-Lengeling Dennis Sheberla Jorge Aguilera‐Iparraguirre Timothy Hirzel Ryan P. Adams Alán Aspuru‐Guzik	6
+ PDF Chat	Molecular graph convolutions: moving beyond fingerprints	2016	Steven Kearnes Kevin McCloskey Marc Berndl Vijay S. Pande Patrick Riley	6
+ PDF Chat	Spin glasses and the statistical mechanics of protein folding.	1987	Joseph D. Bryngelson Peter G. Wolynes	6
+ PDF Chat	Improved coarse-graining of Markov state models via explicit consideration of statistical uncertainty	2012	Gregory R. Bowman	5
+	Neural Message Passing for Quantum Chemistry	2017	Justin Gilmer Samuel S. Schoenholz Patrick Riley Oriol Vinyals George E. Dahl	5
+ PDF Chat	MoleculeNet: a benchmark for molecular machine learning	2017	Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes Caleb Geniesse Aneesh Pappu Karl Leswing Vijay S. Pande	5
+	AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery	2015	Izhar Wallach Michael Dzamba Abraham Heifets	4
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	4
+ PDF Chat	Recommendations for evaluation of computational methods	2008	Ajay N. Jain Anthony Nicholls	4
+ PDF Chat	Variational encoding of complex dynamics	2018	Carlos X. Hernández Hannah K. Wayment-Steele Mohammad M. Sultan Brooke E. Husic Vijay S. Pande	4
+ PDF Chat	Random-Energy Model: Limit of a Family of Disordered Models	1980	Bernard Derrida	4
+	Convolutional Networks on Graphs for Learning Molecular Fingerprints	2015	David Duvenaud Dougal Maclaurin Jorge Aguilera‐Iparraguirre Rafael Gómez‐Bombarelli Timothy Hirzel Alán Aspuru‐Guzik Ryan P. Adams	4
+	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	2015	Sergey Ioffe Christian Szegedy	4
+	One-shot Learning with Memory-Augmented Neural Networks	2016	Adam Santoro Sergey Bartunov Matthew Botvinick Daan Wierstra Timothy Lillicrap	3
+	On the Approximation Quality of Markov State Models	2010	Marco Sarich Frank Noé Christof Schütte	3
+ PDF Chat	PLUMED 2: New feathers for an old bird	2013	Gareth A. Tribello Massimiliano Bonomi Davide Branduardi Carlo Camilloni Giovanni Bussi	3
+ PDF Chat	The NumPy Array: A Structure for Efficient Numerical Computation	2011	Stéfan van der Walt Steven C. Colbert Gaël Varoquaux	3
+ PDF Chat	Funnels, pathways, and the energy landscape of protein folding: A synthesis	1995	Joseph D. Bryngelson José N. Onuchic Nicholas D. Socci Peter G. Wolynes	3
+ PDF Chat	ImageNet Large Scale Visual Recognition Challenge	2015	Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael S. Bernstein	3
+	Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps	2013	Karen Simonyan Andrea Vedaldi Andrew Zisserman	3
+ PDF Chat	Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method	2008	Alessandro Barducci Giovanni Bussi Michele Parrinello	3
+	Mean-Field Model for Protein Folding	1988	Thomas Garel Henri Orland	3
+	Sequence to Sequence Learning with Neural Networks	2014	Ilya Sutskever Oriol Vinyals Quoc V. Le	3
+	Searching for Activation Functions	2017	Prajit Ramachandran Barret Zoph Quoc V. Le	3
+	Neural Message Passing for Quantum Chemistry	2017	Justin Gilmer Samuel S. Schoenholz Patrick Riley Oriol Vinyals George E. Dahl	3
+	Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models	2017	Bowen Liu Bharath Ramsundar Prasad Kawthekar Jade Shi Joseph Gomes Quang Luu Nguyen Stephen Ho Jack L. Sloane Paul A. Wender Vijay S. Pande	3
+	Enumerations of the Hamiltonian walks on a cubic sublattice	1994	Vijay S. Pande Chris Joerg Alexander Y. Grosberg Toyoichi Tanaka	3
+ PDF Chat	ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost	2017	Justin S. Smith Olexandr Isayev Adrián E. Roitberg	3
+	Semi-Supervised Classification with Graph Convolutional Networks	2016	Thomas Kipf Max Welling	3
+ PDF Chat	Molecular enhanced sampling with autoencoders: On‐the‐fly collective variable discovery and accelerated free energy landscape exploration	2018	Wei Chen Andrew L. Ferguson	3
+	Inductive Representation Learning on Large Graphs	2017	William L. Hamilton Rex Ying Jure Leskovec	3
+ PDF Chat	Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics	2018	Christoph Wehmeyer Frank Noé	3
+ PDF Chat	Machine learning of molecular electronic properties in chemical compound space	2013	Grégoire Montavon Matthias Rupp Vivekanand V. Gobre Álvaro Vázquez‐Mayagoitia Katja Hansen Alexandre Tkatchenko Klaus‐Robert Müller O. Anatole von Lilienfeld	3
+	A Time-Independent Free Energy Estimator for Metadynamics	2014	Pratyush Tiwary Michele Parrinello	2
+	Learning to SMILE(S)	2016	Stanisław Jastrzȩbski Damian Leśniak Wojciech Marian Czarnecki	2
+ PDF Chat	Identification of simple reaction coordinates from complex dynamics	2017	Robert T. McGibbon Brooke E. Husic Vijay S. Pande	2
+	Matching Networks for One Shot Learning	2016	Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra	2
+ PDF Chat	Accurate sampling using Langevin dynamics	2007	Giovanni Bussi Michele Parrinello	2
+	Spectral gap optimization of order parameters for sampling complex molecular systems	2016	Pratyush Tiwary B. J. Berne	2
+	An Introduction to the Bootstrap	1994	Bradley Efron Robert Tibshirani	2
+	Neural Machine Translation by Jointly Learning to Align and Translate	2014	Dzmitry Bahdanau Kyunghyun Cho Yoshua Bengio	2
+	TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems	2016	Martı́n Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Gregory S. Corrado Andy Davis Jay B. Dean Matthieu Devin	2
+	Adversarial examples in the physical world	2016	Alexey Kurakin Ian Goodfellow Samy Bengio	2
+	Parallel replica method for dynamics of infrequent events	1998	Arthur F. Voter	2