Computational and Structural Biotechnology Journal,
Год журнала:
2024,
Номер
23, С. 1320 - 1338
Опубликована: Март 24, 2024
Many
research
groups
and
institutions
have
created
a
variety
of
databases
curating
experimental
predicted
data
related
to
protein-ligand
binding.
The
landscape
available
is
dynamic,
with
new
emerging
established
becoming
defunct.
Here,
we
review
the
current
state
that
contain
binding
pockets
interactions.
We
compiled
list
such
databases,
fifty-three
which
are
currently
for
use.
discuss
variation
in
how
defined
summarize
pocket-finding
methods.
organize
into
subgroups
based
on
goals
contents,
describe
standard
use
cases.
also
illustrate
within
same
protein
characterized
differently
across
different
databases.
Finally,
assess
critical
issues
sustainability,
accessibility
redundancy.
Nucleic Acids Research,
Год журнала:
2024,
Номер
52(W1), С. W422 - W431
Опубликована: Апрель 4, 2024
Abstract
ADMETlab
3.0
is
the
second
updated
version
of
web
server
that
provides
a
comprehensive
and
efficient
platform
for
evaluating
ADMET-related
parameters
as
well
physicochemical
properties
medicinal
chemistry
characteristics
involved
in
drug
discovery
process.
This
new
release
addresses
limitations
previous
offers
broader
coverage,
improved
performance,
API
functionality,
decision
support.
For
supporting
data
endpoints,
this
includes
119
features,
an
increase
31
compared
to
version.
The
number
entries
1.5
times
larger
than
with
over
400
000
entries.
incorporates
multi-task
DMPNN
architecture
coupled
molecular
descriptors,
method
not
only
guaranteed
calculation
speed
each
endpoint
simultaneously,
but
also
achieved
superior
performance
terms
accuracy
robustness.
In
addition,
has
been
introduced
meet
growing
demand
programmatic
access
large
amounts
3.0.
Moreover,
uncertainty
estimates
prediction
results,
aiding
confident
selection
candidate
compounds
further
studies
experiments.
publicly
without
need
registration
at:
https://admetlab3.scbdd.com.
Microbiological Research,
Год журнала:
2024,
Номер
286, С. 127822 - 127822
Опубликована: Июнь 26, 2024
Antibiotic
resistance
represents
a
global
health
threat,
challenging
the
efficacy
of
traditional
antimicrobial
agents
and
necessitating
innovative
approaches
to
combat
infectious
diseases.
Among
these
alternatives,
peptides
have
emerged
as
promising
candidates
against
resistant
pathogens.
Unlike
antibiotics
with
only
one
target,
can
use
different
mechanisms
destroy
bacteria,
low
toxicity
mammalian
cells
compared
many
conventional
antibiotics.
Antimicrobial
(AMPs)
encouraging
antibacterial
properties
are
currently
employed
in
clinical
treatment
pathogen
infection,
cancer,
wound
healing,
cosmetics,
or
biotechnology.
This
review
summarizes
discusses
drug
resistance,
limitations
challenges
AMPs
peptide
applications
for
combating
drug-resistant
bacterial
infections,
strategies
enhance
their
capabilities.
Microbial Biotechnology,
Год журнала:
2024,
Номер
17(7)
Опубликована: Июль 1, 2024
The
Global
Burden
of
Disease
report
2019
estimated
14
million
infection-related
deaths,
making
it
the
second
leading
cause
death
after
ischaemic
heart
disease.
Bacterial
pathogens
accounted
for
7.7
deaths
and
attributable
to
bacterial
antibiotic
resistance
amounted
1.3
million,
describing
a
clear
demand
novel
antibiotics.
Antibiotic
development
had
its
golden
age
in
1930-1960.
Following
failures
screening
chemical
libraries
antibiotics
at
beginning
this
century,
high
cost
launching
new
(estimated
US$
1.4
billion
per
registered
drug)
difficulties
achieving
return
investment
antibiotics,
pharmaceutical
industry
has
mostly
left
field.
current
Lilliput
review
analyses
question
whether
scientific
or
economic
hurdles
prevented
registration
Scientifically,
substantial
progress
been
achieved
over
recent
years
define
properties
needed
overcome
permeation
barrier
Gram-negative
pathogens;
extending
space
candidates
by
full
modular
synthesis
suitable
molecules;
bioprospecting
previously
'unculturable'
bacteria
unusual
bacteria;
attacking
targets
on
outer
membrane;
looking
support
from
structural
biology,
genomics,
molecular
genetics,
phylogenetic
deep
machine
learning
approaches.
However,
these
research
activities
were
conducted
academic
researchers
biotech
companies
with
limited
financial
resources.
It
thus
seems
that
frequently
described
as
drying
pipeline,
is
less
lack
insight
than
mobilization
monetary
resources
bring
discoveries
market
despite
push
pull
efforts
public
sector.
npj Antimicrobials and Resistance,
Год журнала:
2025,
Номер
3(1)
Опубликована: Янв. 7, 2025
Artificial
intelligence
(AI)
has
transformed
infectious
disease
control,
enhancing
rapid
diagnosis
and
antibiotic
discovery.
While
conventional
tests
delay
diagnosis,
AI-driven
methods
like
machine
learning
deep
assist
in
pathogen
detection,
resistance
prediction,
drug
These
tools
improve
stewardship
identify
effective
compounds
such
as
antimicrobial
peptides
small
molecules.
This
review
explores
AI
applications
diagnostics,
therapy,
discovery,
emphasizing
both
strengths
areas
needing
improvement.
