Acta Physica Sinica,
Journal Year:
2023,
Volume and Issue:
72(24), P. 240501 - 240501
Published: Jan. 1, 2023
Monoclonal
antibody
inhibitors
targeting
PD-1/PD-L1
immune
checkpoints
are
gradually
entering
the
market
and
have
achieved
certain
positive
effects
in
treatments
of
various
types
tumors.
However,
with
expansion
application,
limitations
drugs
problems
such
as
excessive
homogenization
research
appear,
making
small-molecule
new
focus
researchers.
This
study
aims
to
use
ligand-based
structure-based
binding
activity
prediction
methods
achieve
virtual
screening
PD-L1,
thereby
helping
accelerate
development
small
molecule
drugs.
A
dataset
PD-L1
inhibitory
from
relevant
literature
patents
is
collected
judgment
classification
models
intensity
regression
constructed
based
on
different
molecular
featurization
machine
learning
algorithms.
The
two
filter
68
candidate
compounds
high
a
large
drug-like
pool
(ZINC15).
Ten
these
not
only
good
drug
similarities
pharmacokinetics,
but
also
exhibit
comparable
affinities
similar
mechanisms
action
previous
reported
hotspot
docking.
phenomenon
further
verified
subsequent
dynamics
simulation
estimation
free
energy.
In
this
study,
workflow
integrating
method
developed,
potential
effectively
screened
compound
databases,
which
expected
help
application
tumor
immunotherapy.
Briefings in Bioinformatics,
Journal Year:
2022,
Volume and Issue:
23(4)
Published: July 13, 2022
We
construct
a
protein-protein
interaction
(PPI)
targeted
drug-likeness
dataset
and
propose
deep
molecular
generative
framework
to
generate
novel
molecules
from
the
features
of
seed
compounds.
This
gains
inspiration
published
models,
uses
key
associated
with
PPI
inhibitors
as
input
develops
models
for
de
novo
design
inhibitors.
For
first
time,
quantitative
estimation
index
compounds
targeting
was
applied
evaluation
generation
model
PPI-targeted
Our
results
estimated
that
generated
had
better
drug-likeness.
Additionally,
our
also
exhibits
comparable
performance
other
several
state-of-the-art
molecule
models.
The
share
chemical
space
iPPI-DB
demonstrated
by
analysis.
peptide
characterization-oriented
ligand-based
are
explored.
Finally,
we
recommend
this
will
be
an
important
step
forward
therapeutics.
Journal of Chemical Information and Modeling,
Journal Year:
2023,
Volume and Issue:
64(7), P. 2733 - 2745
Published: June 27, 2023
Since
the
Simplified
Molecular
Input
Line
Entry
System
(SMILES)
is
oriented
to
atomic-level
representation
of
molecules
and
not
friendly
in
terms
human
readability
editable,
however,
IUPAC
closest
natural
language
very
human-oriented
performing
molecular
editing,
we
can
manipulate
generate
corresponding
new
produce
programming-friendly
forms
SMILES.
In
addition,
antiviral
drug
design,
especially
analogue-based
also
more
appropriate
edit
design
directly
from
functional
group
level
than
atomic
SMILES,
since
designing
analogues
involves
altering
R
only,
which
closer
knowledge-based
a
chemist.
Herein,
present
novel
data-driven
self-supervised
pretraining
generative
model
called
"TransAntivirus"
make
select-and-replace
edits
convert
organic
into
desired
properties
for
candidate
analogues.
The
results
indicated
that
TransAntivirus
significantly
superior
control
models
novelty,
validity,
uniqueness,
diversity.
showed
excellent
performance
optimization
nucleoside
non-nucleoside
by
chemical
space
analysis
property
prediction
analysis.
Furthermore,
validate
applicability
drugs,
conducted
two
case
studies
on
screened
four
lead
compounds
against
anticoronavirus
disease
(COVID-19).
Finally,
recommend
this
framework
accelerating
discovery.
International Journal of Molecular Sciences,
Journal Year:
2023,
Volume and Issue:
24(17), P. 13257 - 13257
Published: Aug. 26, 2023
More
than
930,000
protein-protein
interactions
(PPIs)
have
been
identified
in
recent
years,
but
their
physicochemical
properties
differ
from
conventional
drug
targets,
complicating
the
use
of
small
molecules
as
modalities.
Cyclic
peptides
are
a
promising
modality
for
targeting
PPIs,
it
is
difficult
to
predict
structure
target
protein-cyclic
peptide
complex
or
design
cyclic
sequence
that
binds
protein
using
computational
methods.
Recently,
AlphaFold
with
offset
has
enabled
predicting
peptides,
thereby
enabling
de
novo
designs.
We
developed
enable
structural
prediction
proteins
and
complexes
found
AlphaFold2
can
structures
high
accuracy.
also
applied
binder
hallucination
protocol
AfDesign,
method
AlphaFold,
we
could
predicted
local-distance
difference
test
lower
separated
binding
energy
per
unit
interface
area
native
MDM2/p53
structure.
Furthermore,
was
12
other
protein-peptide
one
complex.
Our
approach
shows
possible
putative
sequences
PPI.
Expert Opinion on Drug Discovery,
Journal Year:
2023,
Volume and Issue:
18(7), P. 737 - 752
Published: May 29, 2023
Introduction
Protein-protein
interactions
(PPIs)
have
been
often
considered
undruggable
targets
although
they
are
attractive
for
the
discovery
of
new
therapeutics.
The
spread
artificial
intelligence
and
machine
learning
complemented
with
experimental
methods
is
likely
to
change
perspectives
protein-protein
modulator
research.
Noteworthy,
some
novel
low
molecular
weight
(LMW)
short
peptide
modulators
PPIs
already
in
clinical
trials
treatment
relevant
diseases.Areas
covered
This
review
focuses
on
main
properties
interfaces
key
concepts
pertaining
modulation
PPIs.
authors
survey
recently
reported
state-of-the-art
dealing
rational
design
PPI
highlight
role
several
computer-based
approaches.Expert
opinion
Interfering
specifically
large
protein
still
an
open
challenge.
initial
concerns
about
unfavorable
physicochemical
many
these
nowadays
less
acute
molecules
lying
beyond
rule
5,
orally
available
successful
trials.
As
cost
biologics
interfering
very
high,
it
would
seem
reasonable
put
more
effort,
both
academia
private
sectors,
actively
developing
compounds
peptides
perform
this
task.
Heliyon,
Journal Year:
2023,
Volume and Issue:
9(9), P. e19454 - e19454
Published: Aug. 24, 2023
P-glycoprotein
(P-gp)
is
known
as
the
"multidrug
resistance
protein"
because
it
contributes
to
tumor
several
different
classes
of
anticancer
drugs.
The
effectiveness
such
polymers
in
treating
cancer
and
delivering
drugs
has
been
shown
a
wide
range
vitro
vivo
experiments.
primary
objective
present
study
was
investigate
inhibitory
effects
naturally
occurring
on
P-gp
efflux,
that
inhibition
can
impede
elimination
medications.
our
identify
possess
potential
inhibit
P-gp,
protein
involved
drug
resistance,
with
aim
enhancing
formulations.
ADMET
profile
all
selected
(Agarose,
Alginate,
Carrageenan,
Cyclodextrin,
Dextran,
Hyaluronic
acid,
Polysialic
acid)
studied,
binding
affinities
were
investigated
through
computational
approach
using
recently
released
crystal
structure
PDB
ID:
7O9W.
advanced
also
done
help
molecular
dynamics
simulation.
overcome
MDR
resulting
from
activity
by
when
used
docking
scores
native
ligand,
Agarose,
Chitosan,
acid
found
be
−10.7,
−8.5,
−6.6,
−8.7,
−8.6,
−24.5,
−6.7,
−8.3,
−7.9,
respectively.
It
observed
that,
Cyclodextrin
multiple
properties
delivery
science
here
demonstrated
excellent
affinity.
We
propose
efflux-related
may
prevented
use
Carregeenan,
and/or
administration
Molecules,
Journal Year:
2023,
Volume and Issue:
28(2), P. 501 - 501
Published: Jan. 4, 2023
The
present
work
describes
the
design
and
development
of
seventeen
pyrimidine-clubbed
benzimidazole
derivatives
as
potential
dihydrofolate
reductase
(DHFR)
inhibitors.
These
compounds
were
filtered
by
using
ADMET,
drug-likeness
characteristics
calculations,
molecular
docking
experiments.
Compounds
27,
29,
30,
33,
37,
38,
41
chosen
for
synthesis
based
on
results
in
silico
screening.
Each
synthesized
was
tested
its
vitro
antibacterial
antifungal
activities
a
variety
strains.
All
showed
properties
against
Gram-positive
bacteria
(Staphylococcus
aureus
Staphylococcus
pyogenes)
well
Gram-negative
(Escherichia
coli
Pseudomonas
aeruginosa).
Most
either
had
higher
potency
than
chloramphenicol
or
an
equivalent
to
ciprofloxacin.
29
33
effective
all
bacterial
fungal
Finally,
1,2,3,4-tetrahydropyrimidine-2-thiol
with
6-chloro-2-(chloromethyl)-1H-benzo[d]imidazole
moiety
are
potent
enough
be
considered
promising
lead
discovery
agent.
Biomedicines,
Journal Year:
2022,
Volume and Issue:
10(7), P. 1626 - 1626
Published: July 7, 2022
New
protein-protein
interactions
(PPIs)
are
identified,
but
PPIs
have
different
physicochemical
properties
compared
with
conventional
targets,
making
it
difficult
to
use
small
molecules.
Peptides
offer
a
new
modality
target
PPIs,
designing
appropriate
peptide
sequences
by
computation
is
challenging.
Recently,
AlphaFold
and
RoseTTAFold
made
possible
predict
protein
structures
from
amino
acid
ultra-high
accuracy,
enabling
de
novo
design.
We
designed
peptides
likely
PPI
as
the
using
"binder
hallucination"
protocol
of
AfDesign,
design
method
AlphaFold.
However,
solubility
tended
be
low.
Therefore,
we
loss
function
indices
for
acids
developed
solubility-aware
AfDesign
binder
hallucination
protocol.
The
in
increased
weight
function;
moreover,
they
captured
characteristics
indices.
Moreover,
higher
affinity
than
random
or
single
residue
substitution
when
evaluated
docking
binding
affinity.
Our
approach
shows
that
can
bind
interface
while
controlling
solubility.
Journal of Molecular Biology,
Journal Year:
2024,
Volume and Issue:
436(19), P. 168717 - 168717
Published: July 24, 2024
Amino
acid
scales
are
crucial
for
protein
prediction
tasks,
many
of
them
being
curated
in
the
AAindex
database.
Despite
various
clustering
attempts
to
organize
and
better
understand
their
relationships,
these
approaches
lack
fine-grained
classification
necessary
satisfactory
interpretability
problems.
To
address
this
issue,
we
developed
AAontology—a
two-level
586
amino
(mainly
from
AAindex)
together
with
an
in-depth
analysis
relations—using
bag-of-word-based
classification,
clustering,
manual
refinement
over
multiple
iterations.
AAontology
organizes
physicochemical
into
8
categories
67
subcategories,
enhancing
scale-based
machine
learning
methods
bioinformatics.
Thereby
it
enables
researchers
gain
a
deeper
biological
insight.
We
anticipate
that
will
be
building
block
link
properties
function
dysfunctions
as
well
aid
informed
decision-making
mutation
or
drug
design.