Stem Cell Research & Therapy,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: May 31, 2024
In
the
past
decade,
intestinal
organoid
technology
has
paved
way
for
reproducing
tissue
or
organ
morphogenesis
during
physiological
processes
in
vitro
and
studying
pathogenesis
of
various
diseases.
Intestinal
organoids
are
favored
drug
screening
due
to
their
ability
high-throughput
cultivation
closer
resemblance
patient
genetic
characteristics.
Furthermore,
as
disease
models,
find
wide
applications
diagnostic
markers,
identifying
therapeutic
targets,
exploring
epigenetic
mechanisms
Additionally,
a
transplantable
cellular
system,
have
played
significant
role
reconstruction
damaged
epithelium
conditions
such
ulcerative
colitis
short
bowel
syndrome,
well
material
exchange
metabolic
function
restoration.
The
rise
interdisciplinary
approaches,
including
organoid-on-chip
technology,
genome
editing
techniques,
microfluidics,
greatly
accelerated
development
organoids.
this
review,
VOSviewer
software
is
used
visualize
hot
co-cited
journal
keywords
trends
firstly.
Subsequently,
we
summarized
current
modeling,
screening,
regenerative
medicine.
This
will
deepen
our
understanding
further
explore
intestine
Journal of Infection,
Journal Year:
2023,
Volume and Issue:
87(4), P. 287 - 294
Published: July 17, 2023
BackgroundArtificial
intelligence
(AI),
machine
learning
and
deep
(including
generative
AI)
are
increasingly
being
investigated
in
the
context
of
research
management
human
infection.ObjectivesWe
summarise
recent
potential
future
applications
AI
its
relevance
to
clinical
infection
practice.Methods1,617
PubMed
results
were
screened,
with
priority
given
trials,
systematic
reviews
meta-analyses.
This
narrative
review
focusses
on
studies
using
prospectively
collected
real-world
data
validation,
translational
potential,
such
as
novel
drug
discovery
microbiome-based
interventions.ResultsThere
is
some
evidence
utility
applied
laboratory
diagnostics
(e.g.
digital
culture
plate
reading,
malaria
diagnosis,
antimicrobial
resistance
profiling),
imaging
analysis
pulmonary
tuberculosis
diagnosis),
decision
support
tools
sepsis
prediction,
prescribing)
public
health
outbreak
COVID-19).
Most
date
lack
any
validation
or
metrics.
Significant
heterogeneity
study
design
reporting
limits
comparability.
Many
practical
ethical
issues
exist,
including
algorithm
transparency
risk
bias.ConclusionsInterest
development
AI-based
for
undoubtedly
gaining
pace,
although
appears
much
more
modest.
Artificial Intelligence Chemistry,
Journal Year:
2023,
Volume and Issue:
2(1), P. 100039 - 100039
Published: Dec. 19, 2023
Artificial
intelligence
(AI)
is
revolutionizing
the
current
process
of
drug
design
and
development,
addressing
challenges
encountered
in
its
various
stages.
By
utilizing
AI,
efficiency
significantly
improved
through
enhanced
precision,
reduced
time
cost,
high-performance
algorithms
AI-enabled
computer-aided
(CADD).
Effective
screening
techniques
are
crucial
for
identifying
potential
hit
compounds
from
large
volumes
data
compound
repositories.
The
inclusion
AI
discovery,
including
lead
molecules,
has
proven
to
be
more
effective
than
traditional
vitro
assays.
This
articlereviews
advancements
methods
achieved
AI-enhanced
applications,
machine
learning
(ML),
deep
(DL)
algorithms.
It
specifically
focuses
on
applications
discovery
phase,
exploring
strategies
optimization
such
as
Quantitative
structure-activity
relationship
(QSAR)
modeling,
pharmacophore
de
novo
designing,
high-throughput
virtual
screening.
Valuable
insights
into
different
aspects
discussed,
highlighting
role
AI-based
tools,
pipelines,
case
studies
simplifying
complexities
associated
with
discovery.
Pharmaceutics,
Journal Year:
2024,
Volume and Issue:
16(10), P. 1328 - 1328
Published: Oct. 14, 2024
Artificial
intelligence
(AI)
encompasses
a
broad
spectrum
of
techniques
that
have
been
utilized
by
pharmaceutical
companies
for
decades,
including
machine
learning,
deep
and
other
advanced
computational
methods.
These
innovations
unlocked
unprecedented
opportunities
the
acceleration
drug
discovery
delivery,
optimization
treatment
regimens,
improvement
patient
outcomes.
AI
is
swiftly
transforming
industry,
revolutionizing
everything
from
development
to
personalized
medicine,
target
identification
validation,
selection
excipients,
prediction
synthetic
route,
supply
chain
optimization,
monitoring
during
continuous
manufacturing
processes,
or
predictive
maintenance,
among
others.
While
integration
promises
enhance
efficiency,
reduce
costs,
improve
both
medicines
health,
it
also
raises
important
questions
regulatory
point
view.
In
this
review
article,
we
will
present
comprehensive
overview
AI's
applications
in
covering
areas
such
as
discovery,
safety,
more.
By
analyzing
current
research
trends
case
studies,
aim
shed
light
on
transformative
impact
industry
its
broader
implications
healthcare.
Computer Science & IT Research Journal,
Journal Year:
2024,
Volume and Issue:
5(4), P. 892 - 902
Published: April 17, 2024
This
review
critically
examines
the
integration
of
Machine
Learning
(ML)
in
drug
discovery,
highlighting
its
applications
across
target
identification,
hit
lead
optimization,
and
predictive
toxicology.
Despite
ML's
potential
to
revolutionize
discovery
through
enhanced
efficiency,
accuracy,
novel
insights,
significant
challenges
persist.
These
include
issues
related
data
quality,
model
interpretability,
into
existing
workflows,
regulatory
ethical
considerations.
The
advocates
for
advancements
algorithmic
approaches,
interdisciplinary
collaboration,
improved
data-sharing
practices,
evolving
frameworks
as
solutions
these
challenges.
By
addressing
hurdles
leveraging
capabilities
ML,
process
can
be
significantly
accelerated,
paving
way
development
new
therapeutics.
calls
continued
research,
dialogue
among
stakeholders
realize
transformative
ML
fully.
Keywords:
Learning,
Drug
Discovery,
Predictive
Toxicology,
Data
Quality,
Interdisciplinary
Collaboration.
Cureus,
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 7, 2024
Artificial
Intelligence
(AI)
in
healthcare
marks
a
new
era
of
innovation
and
efficiency,
characterized
by
the
emergence
sophisticated
language
models
such
as
ChatGPT
(OpenAI,
San
Francisco,
CA,
USA),
Gemini
Advanced
(Google
LLC,
Mountain
View,
Co-pilot
(Microsoft
Corp,
Redmond,
WA,
USA).
This
review
explores
transformative
impact
these
AI
technologies
on
various
facets
healthcare,
from
enhancing
patient
care
treatment
protocols
to
revolutionizing
medical
research
tackling
intricate
health
science
challenges.
ChatGPT,
with
its
advanced
natural
processing
capabilities,
leads
way
providing
personalized
mental
support
improving
chronic
condition
management.
extends
boundary
through
data
analytics,
facilitating
early
disease
detection
supporting
decision-making.
Co-pilot,
integrating
seamlessly
systems,
optimizes
clinical
workflows
encourages
culture
among
professionals.
Additionally,
highlights
significant
contributions
accelerating
research,
particularly
genomics
drug
discovery,
thus
paving
path
for
medicine
more
effective
treatments.
The
pivotal
role
epidemiology,
especially
managing
infectious
diseases
COVID-19,
is
also
emphasized,
demonstrating
value
public
strategies.
However,
integration
comes
Concerns
about
privacy,
security,
need
comprehensive
cybersecurity
measures
are
discussed,
along
importance
regulatory
compliance
transparent
consent
management
uphold
ethical
standards
autonomy.
points
out
necessity
seamless
integration,
interoperability,
maintenance
systems'
reliability
accuracy
fully
leverage
AI's
potential
advancing
healthcare.
Intelligent Pharmacy,
Journal Year:
2024,
Volume and Issue:
2(3), P. 367 - 380
Published: Feb. 24, 2024
To
create
novel
treatments
and
treat
complex
diseases,
the
pharmaceutical
sector
is
essential.
Drug
discovery,
however,
a
time-consuming,
pricey,
dangerous
endeavor.
Artificial
intelligence
(AI)
has
become
potent
instrument
that
transformed
several
industries,
including
healthcare,
in
recent
years.
This
summary
gives
general
overview
of
how
AI
expediting
creation
medicines,
revolutionizing
sector,
enabling
drug
discovery.
The
experiencing
discovery
revolution
because
AI.
process
changing
at
different
phases
approaches
like
machine
learning
deep
learning.
abstract
demonstrates
facilitates
development
through
target
identification,
lead
compound
optimization,
design,
repurposing,
clinical
trial
enhancement.
integration
potential
to
hasten
treatments,
save
costs,
improve
patient
outcomes.
fully
realize
research
development,
issues
relating
data
accessibility,
algorithm
interpretability,
laws
must
be
resolved.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 35796 - 35812
Published: Jan. 1, 2024
The
field
of
drug
discovery
has
experienced
a
remarkable
transformation
with
the
advent
artificial
intelligence
(AI)
and
machine
learning
(ML)
technologies.
However,
as
these
AI
ML
models
are
becoming
more
complex,
there
is
growing
need
for
transparency
interpretability
models.
Explainable
Artificial
Intelligence
(XAI)
novel
approach
that
addresses
this
issue
provides
interpretable
understanding
predictions
made
by
In
recent
years,
been
an
increasing
interest
in
application
XAI
techniques
to
discovery.
This
review
article
comprehensive
overview
current
state-of-the-art
discovery,
including
various
methods,
their
challenges
limitations
also
covers
target
identification,
compound
design,
toxicity
prediction.
Furthermore,
suggests
potential
future
research
directions
aims
provide
state
its
transform
field.
Pharmaceutics,
Journal Year:
2025,
Volume and Issue:
17(1), P. 136 - 136
Published: Jan. 19, 2025
Nanosuspensions
(NS),
with
their
submicron
particle
sizes
and
unique
physicochemical
properties,
provide
a
versatile
solution
for
enhancing
the
administration
of
medications
that
are
not
highly
soluble
in
water
or
lipids.
This
review
highlights
recent
advancements,
future
prospects,
challenges
NS-based
drug
delivery,
particularly
oral,
ocular,
transdermal,
pulmonary,
parenteral
routes.
The
conversion
oral
NS
into
powders,
pellets,
granules,
tablets,
capsules,
incorporation
film
dosage
forms
to
address
stability
concerns
is
thoroughly
reviewed.
article
summarizes
key
stabilizers,
polymers,
surfactants,
excipients
used
formulations,
along
ongoing
clinical
trials
patents.
Furthermore,
comprehensive
analysis
various
methods
preparation
provided.
also
explores
vitro
vivo
characterization
techniques,
as
well
scale-down
technologies
bottom-up
preparation.
Selected
examples
commercial
products
discussed.
Rapid
advances
field
could
resolve
issues
related
permeability-limited
absorption
hepatic
first-pass
metabolism,
offering
promise
based
on
proteins
peptides.
evolution
novel
stabilizers
essential
overcome
current
limitations
stability,
bioavailability,
targeting
ability,
safety
profile,
which
ultimately
accelerates
application
commercialization.
