Leveraging artificial intelligence in vaccine development: A narrative review
Journal of Microbiological Methods,
Год журнала:
2024,
Номер
224, С. 106998 - 106998
Опубликована: Июль 15, 2024
Vaccine
development
stands
as
a
cornerstone
of
public
health
efforts,
pivotal
in
curbing
infectious
diseases
and
reducing
global
morbidity
mortality.
However,
traditional
vaccine
methods
are
often
time-consuming,
costly,
inefficient.
The
advent
artificial
intelligence
(AI)
has
ushered
new
era
design,
offering
unprecedented
opportunities
to
expedite
the
process.
This
narrative
review
explores
role
AI
development,
focusing
on
antigen
selection,
epitope
prediction,
adjuvant
identification,
optimization
strategies.
algorithms,
including
machine
learning
deep
learning,
leverage
genomic
data,
protein
structures,
immune
system
interactions
predict
antigenic
epitopes,
assess
immunogenicity,
prioritize
antigens
for
experimentation.
Furthermore,
AI-driven
approaches
facilitate
rational
design
immunogens
identification
novel
candidates
with
optimal
safety
efficacy
profiles.
Challenges
such
data
heterogeneity,
model
interpretability,
regulatory
considerations
must
be
addressed
realize
full
potential
development.
Integrating
emerging
technologies,
single-cell
omics
synthetic
biology,
promises
enhance
precision
scalability.
underscores
transformative
impact
highlights
need
interdisciplinary
collaborations
harmonization
accelerate
delivery
safe
effective
vaccines
against
diseases.
Язык: Английский
Artificial intelligence for drug repurposing against infectious diseases
Artificial Intelligence Chemistry,
Год журнала:
2024,
Номер
2(2), С. 100071 - 100071
Опубликована: Июнь 12, 2024
Traditional
drug
discovery
struggles
to
keep
pace
with
the
ever-evolving
threat
of
infectious
diseases.
New
viruses
and
antibiotic-resistant
bacteria,
all
demand
rapid
solutions.
Artificial
Intelligence
(AI)
offers
a
promising
path
forward
through
accelerated
repurposing.
AI
allows
researchers
analyze
massive
datasets,
revealing
hidden
connections
between
existing
drugs,
disease
targets,
potential
treatments.
This
approach
boasts
several
advantages.
First,
repurposing
drugs
leverages
established
safety
data
reduces
development
time
costs.
Second,
can
broaden
search
for
effective
therapies
by
identifying
unexpected
new
targets.
Finally,
help
mitigate
limitations
predicting
minimizing
side
effects,
optimizing
repurposing,
navigating
intellectual
property
hurdles.
The
article
explores
specific
strategies
like
virtual
screening,
target
identification,
structure
base
design
natural
language
processing.
Real-world
examples
highlight
AI-driven
in
discovering
treatments
Язык: Английский
Towards personalized vaccines
Frontiers in Immunology,
Год журнала:
2024,
Номер
15
Опубликована: Окт. 3, 2024
The
emergence
of
vaccinomics
and
system
vaccinology
represents
a
transformative
shift
in
immunization
strategies,
advocating
for
personalized
vaccines
tailored
to
individual
genetic
immunological
profiles.
Integrating
insights
from
genomics,
transcriptomics,
proteomics,
immunology,
offer
the
promise
enhanced
efficacy
safety,
revolutionizing
field
vaccinology.
However,
development
presents
multifaceted
challenges,
including
technical,
ethical,
economic,
regulatory
considerations.
Addressing
these
challenges
is
essential
ensure
equitable
access
safety
vaccination
strategies.
Despite
hurdles,
potential
optimize
responses
mitigate
disease
burden
underscores
significance
ongoing
research
collaboration
advancing
precision
medicine
immunization.
Язык: Английский
Unleashing the Future: The Revolutionary Role of Machine Learning and Artificial Intelligence in Drug Discovery
European Journal of Pharmacology,
Год журнала:
2024,
Номер
985, С. 177103 - 177103
Опубликована: Ноя. 6, 2024
Язык: Английский
Revolutionizing Pharmaceutical Sciences
Advances in medical technologies and clinical practice book series,
Год журнала:
2024,
Номер
unknown, С. 217 - 246
Опубликована: Дек. 13, 2024
Artificial
Intelligence
(AI)
is
revolutionizing
the
pharmaceutical
sciences
by
significantly
impacting
drug
discovery
and
development,
which
traditionally
a
lengthy,
difficult,
costly
process
often
resulting
in
unrewarded
investments.
AI
has
given
way
accelerating
cycle,
reducing
costs,
integrating
3R
principles
(Replacement,
Reduction,
Refinement).
It
been
instrumental
predicting
drug-target
interactions
(DTIs)
understanding
mechanisms
of
action,
as
demonstrated
AI-DTI
model
successfully
rediscovered
DTIs
for
COVID-19
treatments.
AI's
role
extends
to
toxicity,
bioactivity,
physicochemical
properties,
complementing
conventional
experiments
process.
This
further
supported
machine
learning
(ML)
deep
(DL),
used
computer
facilitated
discovery,
addressing
molecular
design
reaction
prediction.
tools
methodologies
enhance
decision
making,
efficiency
development
improve
human
health
outcomes.
Язык: Английский