Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck
Journal of Chemical Theory and Computation,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 26, 2024
The
weighted
ensemble
(WE)
method
stands
out
as
a
widely
used
segment-based
sampling
technique
renowned
for
its
rigorous
treatment
of
kinetics.
WE
framework
typically
involves
initially
mapping
the
configuration
space
onto
low-dimensional
collective
variable
(CV)
and
then
partitioning
it
into
bins.
efficacy
simulations
heavily
depends
on
selection
CVs
binning
schemes.
recently
proposed
State
Predictive
Information
Bottleneck
(SPIB)
has
emerged
promising
tool
automatically
constructing
from
data
guiding
enhanced
through
an
iterative
manner.
In
this
work,
we
advance
data-driven
pipeline
by
incorporating
prior
expert
knowledge.
Our
hybrid
approach
combines
SPIB-learned
to
enhance
in
explored
regions
with
expert-based
guide
exploration
interest,
synergizing
strengths
both
methods.
Through
benchmarking
alanine
dipeptide
chignoin
systems,
demonstrate
that
our
effectively
guides
sample
states
reduces
run-to-run
variances.
Moreover,
integration
SPIB
model
also
enhances
analysis
interpretation
simulation
identifying
metastable
pathways,
offering
direct
visualization
dynamics.
Язык: Английский
Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics
Digital Discovery,
Год журнала:
2024,
Номер
4(1), С. 211 - 221
Опубликована: Ноя. 28, 2024
We
present
a
graph-based
differentiable
representation
learning
method
from
atomic
coordinates
for
enhanced
sampling
methods
to
learn
both
thermodynamic
and
kinetic
properties
of
system.
Язык: Английский
Advances and Challenges in Milestoning Simulations for Drug–Target Kinetics
Journal of Chemical Theory and Computation,
Год журнала:
2024,
Номер
20(22), С. 9759 - 9769
Опубликована: Ноя. 7, 2024
Molecular
dynamics
simulations
have
become
indispensable
for
exploring
complex
biological
processes,
yet
their
limitations
in
capturing
rare
events
hinder
our
understanding
of
drug–target
kinetics.
In
this
Perspective,
we
investigate
the
domain
milestoning
to
understand
challenge.
The
approach
divides
phase
space
into
discrete
cells,
offering
extended
time
scale
insights.
This
Perspective
traces
history,
applications,
and
future
potential
context
It
explores
fundamental
principles
milestoning,
highlighting
importance
probabilistic
transitions
transition
independence.
Markovian
with
Voronoi
tessellations
is
revisited
address
traditional
challenges.
While
observing
advancements
field,
also
addresses
impending
challenges
estimating
unbinding
rate
constants
through
simulations,
paving
way
more
effective
drug
design
strategies.
Язык: Английский
Analysis of transition rates from variational flooding using analytical theory
The Journal of Chemical Physics,
Год журнала:
2024,
Номер
161(19)
Опубликована: Ноя. 18, 2024
Variational
flooding
is
an
enhanced
sampling
method
for
obtaining
kinetic
rates
from
molecular
dynamics
simulations.
This
inspired
by
the
idea
of
conformational
that
employs
a
boost
potential
acting
along
chosen
reaction
coordinate
to
accelerate
rare
events.
In
this
work,
we
show
how
empirical
distribution
crossing
times
variational
simulations
can
be
modeled
with
analytical
Kramers'
time-dependent
rate
(KTR)
theory.
An
optimized
bias
fills
metastable
free
energy
basins
constructed
variationally
(VES)
method.
VES-derived
then
augmented
switching
function
determines
fill
level
boost.
Having
prescribed
gives
expression
KTR
theory
used
extract
unbiased
rates.
case
static
potential,
barrier
follows
expected
exponential
distribution,
and
are
extracted
series
boosted
at
discrete
levels.
Introducing
increases
gradually
over
simulation
time
leads
simplified
procedure
fitting
biased
We
demonstrate
approach
paradigmatic
cases
alanine
dipeptide
in
vacuum,
asymmetric
SN2
reaction,
folding
chignolin
explicit
solvent.
Язык: Английский
Therapeutic Potential and Mechanistic Insights of a Novel Synthetic α-Lactalbumin-Derived Peptide for the Treatment of Liver Fibrosis
Journal of Clinical and Experimental Hepatology,
Год журнала:
2024,
Номер
15(3), С. 102488 - 102488
Опубликована: Дек. 15, 2024
Язык: Английский
Physically interpretable performance metrics for clustering
The Journal of Chemical Physics,
Год журнала:
2024,
Номер
161(24)
Опубликована: Дек. 26, 2024
Clustering
is
a
type
of
machine
learning
technique,
which
used
to
group
huge
amounts
data
based
on
their
similarity
into
separate
groups
or
clusters.
very
important
task
that
nowadays
analyze
the
and
diverse
amount
coming
out
molecular
dynamics
(MD)
simulations.
Typically,
from
MD
simulations
in
terms
various
frames
trajectory
are
clustered
different
representative
element
each
studied
separately.
Now,
question
this
process
is:
what
quality
clusters
obtained?
There
several
performance
metrics
available
literature
such
as
silhouette
index
Davies–Bouldin
Index
often
clustering.
However,
most
these
focus
overlap
reduced
dimension
for
clustering
do
not
physically
properties
parameters
system.
To
address
issue,
we
have
developed
two
interpretable
scoring
physical
system
analyzing.
We
tested
our
algorithm
three
systems:
(1)
Ising
model,
(2)
peptide
folding
unfolding
WT
HP35,
(3)
protein–ligand
an
enzyme
substrate,
(4)
dissociated
trajectory.
show
provide
us
match
with
intuition
about
systems.
Язык: Английский