MasterMSM: A Package for Constructing Master Equation Models of Molecular Dynamics

Sancho, David De; Aguirre, Anne

doi:10.1021/acs.jcim.9b00468

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…load and featurize domain-specific data files in order to prepare such data for analysis with Deeptime [34][35][36][37][38][39]. Alternatively there also exist time-series analysis packages that are more domain-specific [16,[40][41][42][43] or implement a subset of deeptime's methods but with a wider range of options and/or more flexibility [44][45][46]. As the dynamical model and its properties take the center stage in Deeptime, its aim is also not to perform time-series forecasting, e.g.…”

Section: Introductionmentioning

confidence: 99%

Deeptime: a Python library for machine learning dynamical models from time series data

Hoffmann

Scherer

Hempel

et al. 2021

Mach. Learn.: Sci. Technol.

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Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastable and coherent sets, slow relaxation processes, collective variables, dominant transition pathways or manifolds and channels of probability flow can be of great importance for understanding and characterizing the kinetic, thermodynamic and mechanistic properties of the system. Deeptime is a general purpose Python library offering various tools to estimate dynamical models based on time-series data including conventional linear learning methods, such as Markov state models (MSMs), Hidden Markov Models and Koopman models, as well as kernel and deep learning approaches such as VAMPnets and deep MSMs. The library is largely compatible with scikit-learn, having a range of Estimator classes for these different models, but in contrast to scikit-learn also provides deep Model classes, e.g. in the case of an MSM, which provide a multitude of analysis methods to compute interesting thermodynamic, kinetic and dynamical quantities, such as free energies, relaxation times and transition paths. The library is designed for ease of use but also easily maintainable and extensible code. In this paper we introduce the main features and structure of the deeptime software. Deeptime can be found under https://deeptime-ml.github.io/.

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Section: Introductionmentioning

confidence: 99%

Deeptime: a Python library for machine learning dynamical models from time series data

Hoffmann

Scherer

Hempel

et al. 2021

Mach. Learn.: Sci. Technol.

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“…Using transition path theory 6 , one can recover an intuitive grasp of the mechanism of the rare events of interest. Today, a variety of software packages are available for the construction of MSMs [9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Markov state models from hierarchical density-based assignment

Mitxelena

López

Sancho

2021

The Journal of Chemical Physics

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Markov state models (MSMs) have become one of the preferred methods for the analysis and interpretation of molecular dynamics (MD) simulations of conformational transitions in biopolymers. While there is great variation in terms of implementation, a well-defined workflow involving multiple steps is often adopted. Typically, molecular coordinates are first subjected to dimensionality reduction and then clustered into small “microstates,” which are subsequently lumped into “macrostates” using the information from the slowest eigenmodes. However, the microstate dynamics is often non-Markovian, and long lag times are required to converge the relevant slow dynamics in the MSM. Here, we propose a variation on this typical workflow, taking advantage of hierarchical density-based clustering. When applied to simulation data, this type of clustering separates high population regions of conformational space from others that are rarely visited. In this way, density-based clustering naturally implements assignment of the data based on transitions between metastable states, resulting in a core-set MSM. As a result, the state definition becomes more consistent with the assumption of Markovianity, and the timescales of the slow dynamics of the system are recovered more effectively. We present results of this simplified workflow for a model potential and MD simulations of the alanine dipeptide and the FiP35 WW domain.

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Long-time methods for molecular dynamics simulations: Markov State Models and Milestoning

Narayan

Yuan

Fathizadeh

et al. 2020

Progress in Molecular Biology and Translational Science

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MasterMSM: A Package for Constructing Master Equation Models of Molecular Dynamics

Cited by 5 publications

References 34 publications

Deeptime: a Python library for machine learning dynamical models from time series data

Deeptime: a Python library for machine learning dynamical models from time series data

Markov state models from hierarchical density-based assignment

Long-time methods for molecular dynamics simulations: Markov State Models and Milestoning

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