Machine learning approaches for analyzing and enhancing molecular dynamics simulations

Y Wang, JML Ribeiro, P Tiwary - Current opinion in structural biology, 2020 - Elsevier
Highlights•Machine learning and artificial intelligence approaches have been leveraged for
MD.•One machine learning contribution is in removing noise to make MD data human …

Dynamical reweighting for biased rare event simulations

BG Keller, PG Bolhuis - Annual Review of Physical Chemistry, 2024 - annualreviews.org
Dynamical reweighting techniques aim to recover the correct molecular dynamics from a
simulation at a modified potential energy surface. They are important for unbiasing …

Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)

JML Ribeiro, P Bravo, Y Wang, P Tiwary - The Journal of chemical …, 2018 - pubs.aip.org
Here we propose the reweighted autoencoded variational Bayes for enhanced sampling
(RAVE) method, a new iterative scheme that uses the deep learning framework of variational …

Spectral gap optimization of order parameters for sampling complex molecular systems

P Tiwary, BJ Berne - … of the National Academy of Sciences, 2016 - National Acad Sciences
In modern-day simulations of many-body systems, much of the computational complexity is
shifted to the identification of slowly changing molecular order parameters called collective …

Collective variable-based enhanced sampling and machine learning

M Chen - The European Physical Journal B, 2021 - Springer
Collective variable-based enhanced sampling methods have been widely used to study
thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced …

Advances in automated and reactive flow cytometry for synthetic biotechnology

F Delvigne, JA Martinez - Current Opinion in Biotechnology, 2023 - Elsevier
Highlights•The accessibility to automated flow cytometry equipment has recently been
increased through the open science initiative.•Automated and reactive flow cytometry can be …

The maximum caliber variational principle for nonequilibria

K Ghosh, PD Dixit, L Agozzino… - Annual review of physical …, 2020 - annualreviews.org
Ever since Clausius in 1865 and Boltzmann in 1877, the concepts of entropy and of its
maximization have been the foundations for predicting how material equilibria derive from …

[HTML][HTML] Bridging microscopic and macroscopic mechanisms of p53-MDM2 binding with kinetic network models

G Zhou, GA Pantelopulos, S Mukherjee, VA Voelz - Biophysical journal, 2017 - cell.com
Under normal cellular conditions, the tumor suppressor protein p53 is kept at low levels in
part due to ubiquitination by MDM2, a process initiated by binding of MDM2 to the …

Manifold learning in atomistic simulations: a conceptual review

J Rydzewski, M Chen, O Valsson - Machine Learning: Science …, 2023 - iopscience.iop.org
Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding
meaningful low-dimensional structures hidden in their high-dimensional observations. Such …

Choice of adaptive sampling strategy impacts state discovery, transition probabilities, and the apparent mechanism of conformational changes

MI Zimmerman, JR Porter, X Sun, RR Silva… - Journal of chemical …, 2018 - ACS Publications
Interest in atomically detailed simulations has grown significantly with recent advances in
computational hardware and Markov state modeling (MSM) methods, yet outstanding …