Extra Chance Generalized Hybrid Monte Carlo

Campos, Cédric M.; Sanz‐Serna, J. M.

doi:10.1016/j.jcp.2014.09.037

Cited by 30 publications

(36 citation statements)

References 29 publications

(76 reference statements)

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“…In order to suppress the random walk behaviour induced by the introduction of the accept-reject step needed to ensure the detailed balance it has been suggested to modify the Metropolis acceptreject step [17,18]. The resulting probability distribution satisfies the fixed point equation but drops the detailed balance condition.…”

Section: Look Ahead Hmcmentioning

confidence: 99%

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Testing algorithms for critical slowing down

et al. 2018

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Abstract. We present the preliminary tests on two modifications of the Hybrid Monte Carlo (HMC) algorithm. Both algorithms are designed to travel much farther in the Hamiltonian phase space for each trajectory and reduce the autocorrelations among physical observables thus tackling the critical slowing down towards the continuum limit. We present a comparison of costs of the new algorithms with the standard HMC evolution for pure gauge fields, studying the autocorrelation times for various quantities including the topological charge.

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Section: Look Ahead Hmcmentioning

confidence: 99%

“…Using the notation introduced in section 2.2 the new LAHMC algorithm [17,18] consists in the following steps:…”

Section: Look Ahead Hmcmentioning

confidence: 99%

Testing algorithms for critical slowing down

et al. 2018

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“…In future work, we will examine the bias vs. efficiency trade-offs of our neglect of the shadow work, and the extent to which these can be mitigated by choice of Langevin integrator, or by using reduced-momentum-flipping variants of Hamiltonian Monte Carlo. [62][63][64] While it has been argued more generically that the contribution of "shadow work" in nonequilibrium simulations can be neglected without introducing much bias, 53 this is likely highly dependent on the specific choice of integrator used, so we recommend caution if other integrators are considered.…”

Section: Nonequilibrium Candidate Monte Carlomentioning

confidence: 99%

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

Gill¹,

Lim²,

Grinaway³

et al. 2018

Preprint

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Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragmentlike ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically-relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.

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“…This proof is based on the proof of stationarity given in [8]. Let x = (q, p, z), and let P (x) be the pdf of the target distribution.…”

Section: Proof Of Stationaritymentioning

confidence: 99%

Hamiltonian Monte Carlo with explicit, reversible, and volume-preserving adaptive step size control

Okudo

Suzuki

2017

JSIAM Letters

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Hamiltonian Monte Carlo is a Markov chain Monte Carlo method that uses Hamiltonian dynamics to efficiently produce distant samples. It employs geometric numerical integration to simulate Hamiltonian dynamics, which is a key of its high performance. We present a Hamiltonian Monte Carlo method with adaptive step size control to further enhance the efficiency. We propose a new explicit, reversible, and volume-preserving integration method to adaptively set the step sizes, which does not violate the detailed balance condition or require a large increase in computational time.

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Extra Chance Generalized Hybrid Monte Carlo

Cited by 30 publications

References 29 publications

Testing algorithms for critical slowing down

Testing algorithms for critical slowing down

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

Hamiltonian Monte Carlo with explicit, reversible, and volume-preserving adaptive step size control

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