Advanced quantum and semiclassical methods for simulating photoinduced molecular dynamics and spectroscopy
Molecular‐level understanding of photoinduced processes is critically important for
breakthroughs in transformative technologies utilizing light, ranging from photomedicine to …
breakthroughs in transformative technologies utilizing light, ranging from photomedicine to …
Deeptime: a Python library for machine learning dynamical models from time series data
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 …
from economics to fluid mechanics. In the physical sciences, structures such as metastable …
Understanding and modeling polymers: The challenge of multiple scales
F Schmid - ACS Polymers Au, 2022 - ACS Publications
Polymer materials are multiscale systems by definition. Already the description of a single
macromolecule involves a multitude of scales, and cooperative processes in polymer …
macromolecule involves a multitude of scales, and cooperative processes in polymer …
GraphVAMPNet, using graph neural networks and variational approach to Markov processes for dynamical modeling of biomolecules
Finding a low dimensional representation of data from long-timescale trajectories of
biomolecular processes, such as protein folding or ligand–receptor binding, is of …
biomolecular processes, such as protein folding or ligand–receptor binding, is of …
[HTML][HTML] A small molecule stabilizes the disordered native state of the Alzheimer's Aβ Peptide
The stabilization of native states of proteins is a powerful drug discovery strategy. It is still
unclear, however, whether this approach can be applied to intrinsically disordered proteins …
unclear, however, whether this approach can be applied to intrinsically disordered proteins …
A small molecule stabilizes the disordered native state of the Alzheimer's Aβ peptide
The stabilization of native states of proteins is a powerful drug discovery strategy. It is still
unclear, however, whether this approach can be applied to intrinsically disordered proteins …
unclear, however, whether this approach can be applied to intrinsically disordered proteins …
Deep learning to decompose macromolecules into independent Markovian domains
The increasing interest in modeling the dynamics of ever larger proteins has revealed a
fundamental problem with models that describe the molecular system as being in a global …
fundamental problem with models that describe the molecular system as being in a global …
[HTML][HTML] KIF—key interactions finder: a program to identify the key molecular interactions that regulate protein conformational changes
Simulation datasets of proteins (eg, those generated by molecular dynamics simulations)
are filled with information about how a non-covalent interaction network within a protein …
are filled with information about how a non-covalent interaction network within a protein …
Representation of Protein Dynamics Disentangled by Time-Structure-Based Prior
Representation learning (RL) is a universal technique for deriving low-dimensional
disentangled representations from high-dimensional observations, aiding in a multitude of …
disentangled representations from high-dimensional observations, aiding in a multitude of …
CoVAMPnet: comparative Markov state analysis for studying effects of drug candidates on disordered biomolecules
Computational study of the effect of drug candidates on intrinsically disordered biomolecules
is challenging due to their vast and complex conformational space. Here, we developed a …
is challenging due to their vast and complex conformational space. Here, we developed a …