DeePMD-kit v2: A software package for deep potential models
The Journal of Chemical Physics,
Journal Year:
2023,
Volume and Issue:
159(5)
Published: Aug. 1, 2023
DeePMD-kit
is
a
powerful
open-source
software
package
that
facilitates
molecular
dynamics
simulations
using
machine
learning
potentials
known
as
Deep
Potential
(DP)
models.
This
package,
which
was
released
in
2017,
has
been
widely
used
the
fields
of
physics,
chemistry,
biology,
and
material
science
for
studying
atomistic
systems.
The
current
version
offers
numerous
advanced
features,
such
DeepPot-SE,
attention-based
hybrid
descriptors,
ability
to
fit
tensile
properties,
type
embedding,
model
deviation,
DP-range
correction,
DP
long
range,
graphics
processing
unit
support
customized
operators,
compression,
non-von
Neumann
dynamics,
improved
usability,
including
documentation,
compiled
binary
packages,
graphical
user
interfaces,
application
programming
interfaces.
article
presents
an
overview
major
highlighting
its
features
technical
details.
Additionally,
this
comprehensive
procedure
conducting
representative
application,
benchmarks
accuracy
efficiency
different
models,
discusses
ongoing
developments.
Language: Английский
Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery
Runtong Qian,
No information about this author
Jing Xue,
No information about this author
You Xu
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et al.
Journal of Chemical Information and Modeling,
Journal Year:
2024,
Volume and Issue:
64(19), P. 7214 - 7237
Published: Oct. 3, 2024
Computational
methods
constitute
efficient
strategies
for
screening
and
optimizing
potential
drug
molecules.
A
critical
factor
in
this
process
is
the
binding
affinity
between
candidate
molecules
targets,
quantified
as
free
energy.
Among
various
estimation
methods,
alchemical
transformation
stand
out
their
theoretical
rigor.
Despite
challenges
force
field
accuracy
sampling
efficiency,
advancements
algorithms,
software,
hardware
have
increased
application
of
energy
perturbation
(FEP)
calculations
pharmaceutical
industry.
Here,
we
review
practical
applications
FEP
discovery
projects
since
2018,
covering
both
ligand-centric
residue-centric
transformations.
We
show
that
relative
steadily
achieved
chemical
real-world
applications.
In
addition,
discuss
alternative
physics-based
simulation
incorporation
deep
learning
into
calculations.
Language: Английский
How does machine learning augment alchemical binding free energy calculations?
Future Medicinal Chemistry,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 3
Published: Feb. 8, 2025
Language: Английский
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Journal of Chemical Theory and Computation,
Journal Year:
2025,
Volume and Issue:
unknown
Published: May 2, 2025
In
recent
years,
machine
learning
potentials
(MLPs)
have
become
indispensable
tools
in
physics,
chemistry,
and
materials
science,
driving
the
development
of
software
packages
for
molecular
dynamics
(MD)
simulations
related
applications.
These
packages,
typically
built
on
specific
frameworks,
such
as
TensorFlow,
PyTorch,
or
JAX,
face
integration
challenges
when
advanced
applications
demand
communication
across
different
frameworks.
The
previous
TensorFlow-based
implementation
DeePMD-kit
exemplified
these
limitations.
this
work,
we
introduce
version
3,
a
significant
update
featuring
multibackend
framework
that
supports
PaddlePaddle
backends,
demonstrate
versatility
architecture
through
other
MLP
differentiable
force
fields.
This
allows
seamless
back-end
switching
with
minimal
modifications,
enabling
users
developers
to
integrate
using
innovation
facilitates
more
complex
interoperable
workflows,
paving
way
broader
MLPs
scientific
research.
Language: Английский