High Performance Polymers,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 8, 2025
Polyimide
(PI)
is
widely
used
in
modern
industry
due
to
its
excellent
properties.
Its
synthesis
methods
and
property
research
have
significantly
progressed.
However,
the
design
regulation
of
PI
structures
through
traditional
technologies
are
slow
expensive,
which
make
it
difficult
meet
practical
demand
materials.
With
rapid
development
high-throughput
computing
data-driven
technology,
machine
learning
(ML)
has
become
an
important
method
for
exploring
new
Data-driven
ML
envisaged
as
a
decisive
enabler
PIs
discovery.
This
paper
first
introduces
basic
workflow
common
algorithms
ML.
Secondly,
applications
material
properties
prediction,
assisting
computational
simulation
inverse
desired
reviewed.
Finally,
we
discuss
main
challenges
possible
solutions
research.
Acta Materia Medica,
Journal Year:
2025,
Volume and Issue:
4(1)
Published: Jan. 1, 2025
The
binding
affinity
of
aptamers
to
targets
has
a
crucial
role
in
the
pharmaceutical
and
biosensing
effects.
Despite
diverse
post-systematic
evolution
ligands
by
exponential
enrichment
(post-SELEX)
modifications
explored
aptamer
optimization,
accurate
prediction
high-affinity
modification
strategies
remains
challenging.
Sclerostin,
which
antagonizes
Wnt
signaling
pathway,
negatively
regulates
bone
formation.
Our
screened
sclerostin
was
previously
shown
exert
anabolic
potential.
In
current
study,
an
interactive
methodology
involving
exchange
mutual
information
between
experimental
endeavors
machine
learning
initially
proposed
design
post-SELEX
strategy
for
aptamers.
After
four
rounds
training
(a
total
422
modified
aptamer-target
datasets
with
types
sites),
antifcial
intelligence
model
high
predictive
accuracy
correlation
coefficient
0.82
predicted
actual
affinities
obtained.
Notably,
learning-powered
selected
from
this
work
exhibited
105-fold
higher
(picomole
level
K
D
value)
3.2-folds
greater
Wnt-signal
re-activation
effect
compared
naturally
unmodified
This
approach
harnessed
power
predict
most
promising
