Molecular Therapy — Nucleic Acids,
Год журнала:
2024,
Номер
35(3), С. 102295 - 102295
Опубликована: Авг. 8, 2024
Due
to
the
transformation
of
artificial
intelligence
(AI)
tools
and
technologies,
AI-driven
drug
discovery
has
come
forefront.
It
reduces
time
expenditure.
these
advantages,
pharmaceutical
industries
are
concentrating
on
discovery.
Several
molecules
have
been
discovered
using
AI-based
techniques
tools,
several
newly
AI-discovered
already
entered
clinical
trials.
In
this
review,
we
first
present
data
their
resources
in
sector
for
illustrated
some
significant
algorithms
or
used
AI
ML
which
field.
We
gave
an
overview
deep
neural
network
(NN)
models
compared
them
with
NNs.
Then,
illustrate
recent
advancement
landscape
learning,
such
as
identification
targets,
prediction
structure,
estimation
drug-target
interaction,
binding
affinity,
design
Signal Transduction and Targeted Therapy,
Год журнала:
2023,
Номер
8(1)
Опубликована: Март 14, 2023
Abstract
AlphaFold2
(AF2)
is
an
artificial
intelligence
(AI)
system
developed
by
DeepMind
that
can
predict
three-dimensional
(3D)
structures
of
proteins
from
amino
acid
sequences
with
atomic-level
accuracy.
Protein
structure
prediction
one
the
most
challenging
problems
in
computational
biology
and
chemistry,
has
puzzled
scientists
for
50
years.
The
advent
AF2
presents
unprecedented
progress
protein
attracted
much
attention.
Subsequent
release
more
than
200
million
predicted
further
aroused
great
enthusiasm
science
community,
especially
fields
medicine.
thought
to
have
a
significant
impact
on
structural
research
areas
need
information,
such
as
drug
discovery,
design,
function,
et
al.
Though
time
not
long
since
was
developed,
there
are
already
quite
few
application
studies
medicine,
many
them
having
preliminarily
proved
potential
AF2.
To
better
understand
promote
its
applications,
we
will
this
article
summarize
principle
architecture
well
recipe
success,
particularly
focus
reviewing
applications
Limitations
current
also
be
discussed.
Trends in Pharmacological Sciences,
Год журнала:
2023,
Номер
44(9), С. 561 - 572
Опубликована: Июль 19, 2023
Disease
modeling
and
target
identification
are
the
most
crucial
initial
steps
in
drug
discovery,
influence
probability
of
success
at
every
step
development.
Traditional
is
a
time-consuming
process
that
takes
years
to
decades
usually
starts
an
academic
setting.
Given
its
advantages
analyzing
large
datasets
intricate
biological
networks,
artificial
intelligence
(AI)
playing
growing
role
modern
identification.
We
review
recent
advances
focusing
on
breakthroughs
AI-driven
therapeutic
exploration.
also
discuss
importance
striking
balance
between
novelty
confidence
selection.
An
increasing
number
AI-identified
targets
being
validated
through
experiments
several
AI-derived
drugs
entering
clinical
trials;
we
highlight
current
limitations
potential
pathways
for
moving
forward.
Nature Biotechnology,
Год журнала:
2024,
Номер
unknown
Опубликована: Март 8, 2024
Abstract
Idiopathic
pulmonary
fibrosis
(IPF)
is
an
aggressive
interstitial
lung
disease
with
a
high
mortality
rate.
Putative
drug
targets
in
IPF
have
failed
to
translate
into
effective
therapies
at
the
clinical
level.
We
identify
TRAF2-
and
NCK-interacting
kinase
(TNIK)
as
anti-fibrotic
target
using
predictive
artificial
intelligence
(AI)
approach.
Using
AI-driven
methodology,
we
generated
INS018_055,
small-molecule
TNIK
inhibitor,
which
exhibits
desirable
drug-like
properties
activity
across
different
organs
vivo
through
oral,
inhaled
or
topical
administration.
INS018_055
possesses
anti-inflammatory
effects
addition
its
profile,
validated
multiple
studies.
Its
safety
tolerability
well
pharmacokinetics
were
randomized,
double-blinded,
placebo-controlled
phase
I
trial
(NCT05154240)
involving
78
healthy
participants.
A
separate
China,
CTR20221542,
also
demonstrated
comparable
pharmacokinetic
profiles.
This
work
was
completed
roughly
18
months
from
discovery
preclinical
candidate
nomination
demonstrates
capabilities
of
our
generative
drug-discovery
pipeline.
The Annual Review of Pharmacology and Toxicology,
Год журнала:
2023,
Номер
64(1), С. 527 - 550
Опубликована: Сен. 22, 2023
Drug
discovery
is
adapting
to
novel
technologies
such
as
data
science,
informatics,
and
artificial
intelligence
(AI)
accelerate
effective
treatment
development
while
reducing
costs
animal
experiments.
AI
transforming
drug
discovery,
indicated
by
increasing
interest
from
investors,
industrial
academic
scientists,
legislators.
Successful
requires
optimizing
properties
related
pharmacodynamics,
pharmacokinetics,
clinical
outcomes.
This
review
discusses
the
use
of
in
three
pillars
discovery:
diseases,
targets,
therapeutic
modalities,
with
a
focus
on
small
molecule
drugs.
technologies,
generative
chemistry,
machine
learning,
multi-property
optimization,
have
enabled
several
compounds
enter
trials.
The
scientific
community
must
carefully
vet
known
information
address
reproducibility
crisis.
full
potential
can
only
be
realized
sufficient
ground
truth
appropriate
human
intervention
at
later
pipeline
stages.
Big Data and Cognitive Computing,
Год журнала:
2023,
Номер
7(3), С. 147 - 147
Опубликована: Авг. 30, 2023
The
future
of
innovative
robotic
technologies
and
artificial
intelligence
(AI)
in
pharmacy
medicine
is
promising,
with
the
potential
to
revolutionize
various
aspects
health
care.
These
advances
aim
increase
efficiency,
improve
patient
outcomes,
reduce
costs
while
addressing
pressing
challenges
such
as
personalized
need
for
more
effective
therapies.
This
review
examines
major
robotics
AI
pharmaceutical
medical
fields,
analyzing
advantages,
obstacles,
implications
In
addition,
prominent
organizations
research
institutions
leading
way
these
technological
advancements
are
highlighted,
showcasing
their
pioneering
efforts
creating
utilizing
state-of-the-art
solutions
medicine.
By
thoroughly
current
state
care
exploring
possibilities
further
progress,
this
work
aims
provide
readers
a
comprehensive
understanding
transformative
power
evolution
healthcare
sector.
Striking
balance
between
embracing
technology
preserving
human
touch,
investing
R&D,
establishing
regulatory
frameworks
within
ethical
guidelines
will
shape
systems.
seamless
integration
systems
benefit
patients
providers.
Journal of Cheminformatics,
Год журнала:
2024,
Номер
16(1)
Опубликована: Фев. 21, 2024
REINVENT
4
is
a
modern
open-source
generative
AI
framework
for
the
design
of
small
molecules.
The
software
utilizes
recurrent
neural
networks
and
transformer
architectures
to
drive
molecule
generation.
These
generators
are
seamlessly
embedded
within
general
machine
learning
optimization
algorithms,
transfer
learning,
reinforcement
curriculum
learning.
enables
facilitates
de
novo
design,
R-group
replacement,
library
linker
scaffold
hopping
optimization.
This
contribution
gives
an
overview
describes
its
design.
Algorithms
their
applications
discussed
in
detail.
command
line
tool
which
reads
user
configuration
either
TOML
or
JSON
format.
aim
this
release
provide
reference
implementations
some
most
common
algorithms
based
An
additional
goal
with
create
education
future
innovation
molecular
available
from
https://github.com/MolecularAI/REINVENT4
released
under
permissive
Apache
2.0
license.
Scientific
contribution.
provides
implementation
where
also
being
used
production
support
in-house
drug
discovery
projects.
publication
one
code
full
documentation
thereof
will
increase
transparency
foster
innovation,
collaboration
education.
Drug Discovery Today,
Год журнала:
2023,
Номер
28(8), С. 103675 - 103675
Опубликована: Июнь 17, 2023
In
recent
years,
drug
discovery
and
life
sciences
have
been
revolutionized
with
machine
learning
artificial
intelligence
(AI)
methods.
Quantum
computing
is
touted
to
be
the
next
most
significant
leap
in
technology;
one
of
main
early
practical
applications
for
quantum
solutions
predicted
chemistry
simulations.
Here,
we
review
near-term
their
advantages
generative
highlight
challenges
that
can
addressed
noisy
intermediate-scale
(NISQ)
devices.
We
also
discuss
possible
integration
systems
running
on
computers
into
established
AI
platforms.
Molecular Biomedicine,
Год журнала:
2025,
Номер
6(1)
Опубликована: Янв. 3, 2025
Abstract
Integrating
Artificial
Intelligence
(AI)
across
numerous
disciplines
has
transformed
the
worldwide
landscape
of
pandemic
response.
This
review
investigates
multidimensional
role
AI
in
pandemic,
which
arises
as
a
global
health
crisis,
and
its
preparedness
responses,
ranging
from
enhanced
epidemiological
modelling
to
acceleration
vaccine
development.
The
confluence
technologies
guided
us
new
era
data-driven
decision-making,
revolutionizing
our
ability
anticipate,
mitigate,
treat
infectious
illnesses.
begins
by
discussing
impact
on
emerging
countries
worldwide,
elaborating
critical
significance
modelling,
bringing
enabling
forecasting,
mitigation
response
pandemic.
In
epidemiology,
AI-driven
models
like
SIR
(Susceptible-Infectious-Recovered)
SIS
(Susceptible-Infectious-Susceptible)
are
applied
predict
spread
disease,
preventing
outbreaks
optimising
distribution.
also
demonstrates
how
Machine
Learning
(ML)
algorithms
predictive
analytics
improve
knowledge
disease
propagation
patterns.
collaborative
aspect
discovery
clinical
trials
various
vaccines
is
emphasised,
focusing
constructing
AI-powered
surveillance
networks.
Conclusively,
presents
comprehensive
assessment
impacts
builds
AI-enabled
dynamic
collaborating
ML
Deep
(DL)
techniques,
develops
implements
trials.
focuses
screening,
contact
tracing
monitoring
virus-causing
It
advocates
for
sustained
research,
real-world
implications,
ethical
application
strategic
integration
strengthen
collective
face
alleviate
effects
issues.
