Antibiotics,
Год журнала:
2020,
Номер
9(8), С. 455 - 455
Опубликована: Июль 28, 2020
Oceans
cover
seventy
percent
of
the
planet’s
surface
and
besides
being
an
immense
reservoir
biological
life,
they
serve
as
vital
sources
for
human
sustenance,
tourism,
transport
commerce.
Yet,
it
is
estimated
by
National
Oceanic
Atmospheric
Administration
(NOAA)
that
eighty
oceans
remain
unexplored.
The
untapped
resources
present
in
may
be
fundamental
solving
several
world’s
public
health
crises
21st
century,
which
span
from
rise
antibiotic
resistance
bacteria,
pathogenic
fungi
parasites,
to
cancer
incidence
viral
infection
outbreaks.
In
this
review,
risks
well
how
marine
bacterial
derived
natural
products
tools
fight
them
will
discussed.
Moreover,
overview
made
research
pipeline
novel
molecules,
identification
bioactive
crude
extracts
isolation
chemical
characterization
molecules
within
framework
One
Health
approach.
This
review
highlights
information
has
been
published
since
2014,
showing
current
relevance
bacteria
discovery
products.
Marine Drugs,
Год журнала:
2023,
Номер
21(5), С. 308 - 308
Опубликована: Май 19, 2023
Natural
Products
(NP)
are
essential
for
the
discovery
of
novel
drugs
and
products
numerous
biotechnological
applications.
The
NP
process
is
expensive
time-consuming,
having
as
major
hurdles
dereplication
(early
identification
known
compounds)
structure
elucidation,
particularly
determination
absolute
configuration
metabolites
with
stereogenic
centers.
This
review
comprehensively
focuses
on
recent
technological
instrumental
advances,
highlighting
development
methods
that
alleviate
these
obstacles,
paving
way
accelerating
towards
Herein,
we
emphasize
most
innovative
high-throughput
tools
advancing
bioactivity
screening,
chemical
analysis,
dereplication,
metabolite
profiling,
metabolomics,
genome
sequencing
and/or
genomics
approaches,
databases,
bioinformatics,
chemoinformatics,
three-dimensional
elucidation.
Computational and Structural Biotechnology Journal,
Год журнала:
2025,
Номер
27, С. 423 - 439
Опубликована: Янв. 1, 2025
Antimicrobial
resistance
(AMR)
is
a
major
threat
to
global
public
health.
The
current
review
synthesizes
address
the
possible
role
of
Artificial
Intelligence
and
Machine
Learning
(AI/ML)
in
mitigating
AMR.
Supervised
learning,
unsupervised
deep
reinforcement
natural
language
processing
are
some
main
tools
used
this
domain.
AI/ML
models
can
use
various
data
sources,
such
as
clinical
information,
genomic
sequences,
microbiome
insights,
epidemiological
for
predicting
AMR
outbreaks.
Although
relatively
new
fields,
numerous
case
studies
offer
substantial
evidence
their
successful
application
outbreaks
with
greater
accuracy.
These
provide
insights
into
discovery
novel
antimicrobials,
repurposing
existing
drugs,
combination
therapy
through
analysis
molecular
structures.
In
addition,
AI-based
decision
support
systems
real-time
guide
healthcare
professionals
improve
prescribing
antibiotics.
also
outlines
how
AI
surveillance,
analyze
trends,
enable
early
outbreak
identification.
Challenges,
ethical
considerations,
privacy,
model
biases
exist,
however,
continuous
development
methodologies
enables
play
significant
combating
Journal of Medicinal Chemistry,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 6, 2025
Drug
discovery,
a
multifaceted
process
from
compound
identification
to
regulatory
approval,
historically
plagued
by
inefficiencies
and
time
lags
due
limited
data
utilization,
now
faces
urgent
demands
for
accelerated
lead
identification.
Innovations
in
biological
computational
chemistry
have
spurred
shift
trial-and-error
methods
holistic
approaches
medicinal
chemistry.
Computational
techniques,
particularly
artificial
intelligence
(AI),
notably
machine
learning
(ML)
deep
(DL),
revolutionized
drug
development,
enhancing
analysis
predictive
modeling.
Natural
products
(NPs)
long
served
as
rich
sources
of
biologically
active
compounds,
with
many
successful
drugs
originating
them.
Advances
information
science
expanded
NP-related
databases,
enabling
deeper
exploration
AI.
Integrating
AI
into
NP
discovery
promises
discoveries,
leveraging
AI's
analytical
prowess,
including
generative
synthesis.
This
perspective
illuminates
current
landscape
addressing
strengths,
limitations,
future
trajectories
advance
this
vital
research
domain.
Molecular Informatics,
Год журнала:
2020,
Номер
39(12)
Опубликована: Июль 29, 2020
This
review
seeks
to
provide
a
timely
survey
of
the
scope
and
limitations
cheminformatics
methods
in
natural
product-based
drug
discovery.
Following
an
overview
data
resources
chemical,
biological
structural
information
on
products,
we
discuss,
among
other
aspects,
silico
for
(i)
curation
products
dereplication,
(ii)
analysis,
visualization,
navigation
comparison
chemical
space,
(iii)
quantification
product-likeness,
(iv)
prediction
bioactivities
(virtual
screening,
target
prediction),
ADME
safety
profiles
(toxicity)
(v)
products-inspired
de
novo
design
(vi)
prone
cause
interference
with
assays.
Among
many
discussed
are
rule-based,
similarity-based,
shape-based,
pharmacophore-based
network-based
approaches,
docking
machine
learning
methods.
Natural Product Reports,
Год журнала:
2021,
Номер
38(11), С. 2041 - 2065
Опубликована: Янв. 1, 2021
Here
we
provide
a
comprehensive
guide
for
studying
natural
product
biosynthesis
using
genomics,
metabolomics,
and
their
integrated
datasets.
We
emphasize
strategies
critical
outlook
on
remaining
challenges
in
the
field.
Frontiers in Chemistry,
Год журнала:
2021,
Номер
9
Опубликована: Апрель 29, 2021
Natural
products
are
continually
explored
in
the
development
of
new
bioactive
compounds
with
industrial
applications,
attracting
attention
scientific
research
efforts
due
to
their
pharmacophore-like
structures,
pharmacokinetic
properties,
and
unique
chemical
space.
The
systematic
search
for
natural
sources
obtain
valuable
molecules
develop
commercial
value
purposes
remains
most
challenging
task
bioprospecting.
Virtual
screening
strategies
have
innovated
discovery
novel
assessing
silico
large
compound
libraries,
favoring
analysis
space,
pharmacodynamics,
thus
leading
reduction
financial
efforts,
infrastructure,
time
involved
process
discovering
entities.
Herein,
we
discuss
computational
approaches
methods
developed
explore
chemo-structural
diversity
products,
focusing
on
main
paradigms
from
sources,
placing
particular
emphasis
artificial
intelligence,
cheminformatics
methods,
big
data
analyses.
