@article{HuberKim1996WeightedEnsemble,
  abstract = {A new method, weighted-ensemble Brownian dynamics, is proposed for the simulation of protein-association reactions and other events whose frequencies of outcomes are constricted by free energy barriers. The method features a weighted ensemble of trajectories in configuration space with energy levels dictating the proper correspondence between "particles" and probability. Instead of waiting a very long time for an unlikely event to occur, the probability packets are split, and small packets of probability are allowed to diffuse almost immediately into regions of configuration space that are less likely to be sampled. The method has been applied to the Northrup and Erickson (1992) model of docking-type diffusion-limited reactions and yields reaction rate constants in agreement with those obtained by direct Brownian simulation, but at a fraction of the CPU time (10(-4) to 10(-3), depending on the model). Because the method is essentially a variant of standard Brownian dynamics algorithms, it is anticipated that weighted-ensemble Brownian dynamics, in conjunction with biophysical force models, can be applied to a large class of association reactions of interest to the biophysics community.},
  author = {Huber, G A and Kim, S},
  journal = {Biophysical Journal},
  misc = { pmid = {8770190}, doi = {10.1016/S0006-3495(96)79552-8}},
  month = {jan},
  number = {1},
  pages = {97-110},
  title = {Weighted-ensemble Brownian dynamics simulations for protein association reactions},
  url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1224912/},
  volume = {70},
  year = {1996},
}

@article{ZwierChong2015WESTPA,
  abstract = { The weighted ensemble (WE) path sampling approach orchestrates an ensemble of parallel calculations with intermittent communication to enhance the sampling of rare events, such as molecular associations or conformational changes in proteins or peptides. Trajectories are replicated and pruned in a way that focuses computational effort on underexplored regions of configuration space while maintaining rigorous kinetics. To enable the simulation of rare events at any scale (e.g., atomistic, cellular), we have developed an open-source, interoperable, and highly scalable software package for the execution and analysis of WE simulations: WESTPA (The Weighted Ensemble Simulation Toolkit with Parallelization and Analysis). WESTPA scales to thousands of CPU cores and includes a suite of analysis tools that have been implemented in a massively parallel fashion. The software has been designed to interface conveniently with any dynamics engine and has already been used with a variety of molecular dynamics (e.g., GROMACS, NAMD, OpenMM, AMBER) and cell-modeling packages (e.g., BioNetGen, MCell). WESTPA has been in production use for over a year, and its utility has been demonstrated for a broad set of problems, ranging from atomically detailed host–guest associations to nonspatial chemical kinetics of cellular signaling networks. The following describes the design and features of WESTPA, including the facilities it provides for running WE simulations and storing and analyzing WE simulation data, as well as examples of input and output. },
  author = {Zwier, Matthew C. and Adelman, Joshua L. and Kaus, Joseph W. and Pratt, Adam J. and Wong, Kim F. and Rego, Nicholas B. and Suárez, Ernesto and Lettieri, Steven and Wang, David W. and Grabe, Michael and Zuckerman, Daniel M. and Chong, Lillian T.},
  journal = {Journal of Chemical Theory and Computation},
  misc = { eprint = {http://dx.doi.org/10.1021/ct5010615}, doi = {10.1021/ct5010615}},
  number = {2},
  pages = {800-809},
  title = {WESTPA: An Interoperable, Highly Scalable Software Package for Weighted Ensemble Simulation and Analysis},
  url = {/brokenurl# http://dx.doi.org/10.1021/ct5010615 },
  volume = {11},
  year = {2015},
}

@article{AbdulWahid2014AWEWQ,
  abstract = { A limitation of traditional molecular dynamics (MD) is that reaction rates are difficult to compute. This is due to the rarity of observing transitions between metastable states since high energy barriers trap the system in these states. Recently the weighted ensemble (WE) family of methods have emerged which can flexibly and efficiently sample conformational space without being trapped and allow calculation of unbiased rates. However, while WE can sample correctly and efficiently, a scalable implementation applicable to interesting biomolecular systems is not available. We provide here a GPLv2 implementation called AWE-WQ of a WE algorithm using the master/worker distributed computing WorkQueue (WQ) framework. AWE-WQ is scalable to thousands of nodes and supports dynamic allocation of computer resources, heterogeneous resource usage (such as central processing units (CPU) and graphical processing units (GPUs) concurrently), seamless heterogeneous cluster usage (i.e., campus grids and cloud providers), and support for arbitrary MD codes such as GROMACS, while ensuring that all statistics are unbiased. We applied AWE-WQ to a 34 residue protein which simulated 1.5 ms over 8 months with peak aggregate performance of 1000 ns/h. Comparison was done with a 200 μs simulation collected on a GPU over a similar timespan. The folding and unfolded rates were of comparable accuracy. },
  author = {Abdul-Wahid, Badi’ and Feng, Haoyun and Rajan, Dinesh and Costaouec, Ronan and Darve, Eric and Thain, Douglas and Izaguirre, Jesús A.},
  journal = {Journal of Chemical Information and Modeling},
  misc = { eprint = {http://dx.doi.org/10.1021/ci500321g}, doi = {10.1021/ci500321g}},
  note = {PMID: 25207854},
  number = {10},
  pages = {3033-3043},
  title = {AWE-WQ: Fast-Forwarding Molecular Dynamics Using the Accelerated Weighted Ensemble},
  url = {http://dx.doi.org/10.1021/ci500321g},
  volume = {54},
  year = {2014},
}

@article{Dickson2014WExplore,
  abstract = {As most relevant motions in biomolecular systems are inaccessible to conventional molecular dynamics simulations, algorithms that enhance sampling of rare events are indispensable. Increasing interest in intrinsically disordered systems and the desire to target ensembles of protein conformations (rather than single structures) in drug development motivate the need for enhanced sampling algorithms that are not limited to "two-basin" problems, and can efficiently determine structural ensembles. For systems that are not well-studied, this must often be done with little or no information about the dynamics of interest. Here we present a novel strategy to determine structural ensembles that uses dynamically defined sampling regions that are organized in a hierarchical framework. It is based on the weighted ensemble algorithm, where an ensemble of copies of the system ("replicas") is directed to new regions of configuration space through merging and cloning operations. The sampling hierarchy allows for a large number of regions to be defined, while using only a small number of replicas that can be balanced over multiple length scales. We demonstrate this algorithm on two model systems that are analytically solvable and examine the 10-residue peptide chignolin in explicit solvent. The latter system is analyzed using a configuration space network, and novel hydrogen bonds are found that facilitate folding.},
  author = {Dickson, A and Brooks, C L},
  journal = {Journal of Physical Chemistry B},
  misc = { pmid = {24490961}, doi = {10.1021/jp411479c}},
  month = {apr},
  number = {13},
  pages = {3532-3542},
  title = {WExplore: hierarchical exploration of high-dimensional spaces using the weighted ensemble algorithm},
  url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4404516/},
  volume = {118},
  year = {2014},
}

@article{Donyapour2019REVO,
  author = {Donyapour, Nazanin and Roussey, Nicole M and Dickson, Alex},
  journal = {The Journal of Chemical Physics},
  number = {24},
  pages = {244112},
  publisher = {AIP Publishing},
  title = {REVO: Resampling of ensembles by variation optimization},
  volume = {150},
  year = {2019},
}

@article{Eastman2013OpenMM4,
  abstract = {OpenMM is a software toolkit for performing molecular simulations on a range of high performance computing architectures. It is based on a layered architecture: the lower layers function as a reusable library that can be invoked by any application, while the upper layers form a complete environment for running molecular simulations. The library API hides all hardware-specific dependencies and optimizations from the users and developers of simulation programs: they can be run without modification on any hardware on which the API has been implemented. The current implementations of OpenMM include support for graphics processing units using the OpenCL and CUDA frameworks. In addition, OpenMM was designed to be extensible, so new hardware architectures can be accommodated and new functionality (e.g., energy terms and integrators) can be easily added.},
  author = {Eastman, P and Friedrichs, M S and Chodera, J D and Radmer, R J and Bruns, C M and Ku, J P and Beauchamp, K A and Lane, T J and Wang, L P and Shukla, D and Tye, T and Houston, M and Stich, T and Klein, C and Shirts, M R and Pande, V S},
  journal = {Journal of Chemical Theory and Computation},
  misc = { pmid = {23316124}, doi = {10.1021/ct300857j}},
  month = {jan},
  number = {1},
  pages = {461-469},
  title = {OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3539733/},
  volume = {9},
  year = {2013},
}

