Nanomaterials,
Journal Year:
2020,
Volume and Issue:
10(4), P. 708 - 708
Published: April 8, 2020
Transcriptomics
data
are
relevant
to
address
a
number
of
challenges
in
Toxicogenomics
(TGx).
After
careful
planning
exposure
conditions
and
preprocessing,
the
TGx
can
be
used
predictive
toxicology,
where
more
advanced
modelling
techniques
applied.
The
large
volume
molecular
profiles
produced
by
omics-based
technologies
allows
development
application
artificial
intelligence
(AI)
methods
TGx.
Indeed,
publicly
available
omics
datasets
constantly
increasing
together
with
plethora
different
that
made
facilitate
their
analysis,
interpretation
generation
accurate
stable
models.
In
this
review,
we
present
state-of-the-art
applied
transcriptomics
We
show
how
benchmark
dose
(BMD)
analysis
data.
review
read
across
adverse
outcome
pathways
(AOP)
methodologies.
discuss
network-based
approaches
successfully
employed
clarify
mechanism
action
(MOA)
or
specific
biomarkers
exposure.
also
describe
main
AI
methodologies
create
classification
regression
models
current
challenges.
Finally,
short
description
deep
learning
(DL)
integration
these
contexts.
Modelling
represents
valuable
tool
for
chemical
safety
assessment.
This
is
third
part
three-article
series
on
Toxicogenomics.
Energy & Environmental Science,
Journal Year:
2020,
Volume and Issue:
14(1), P. 90 - 105
Published: Nov. 26, 2020
In
this
review,
current
research
status
about
the
machine
learning
use
in
organic
solar
cell
is
reviewed.
We
have
discussed
challenges
anticipating
data
driven
material
design.
ACS Omega,
Journal Year:
2020,
Volume and Issue:
5(26), P. 16076 - 16084
Published: June 25, 2020
Natural
products
continue
to
be
major
sources
of
bioactive
compounds
and
drug
candidates
not
only
because
their
unique
chemical
structures
but
also
overall
favorable
metabolism
pharmacokinetic
properties.
The
number
publicly
accessible
natural
product
databases
has
increased
significantly
in
the
past
few
years.
However,
systematic
ADME/Tox
profile
been
reported
on
a
limited
basis.
For
instance,
BIOFACQUIM
was
recently
published
as
public
database
from
Mexico,
country
with
rich
source
biomolecules.
its
reported.
Herein,
we
discuss
results
an
in-depth
silico
other
large
collections
products.
It
concluded
that
absorption
distribution
profiles
are
similar
those
approved
drugs,
while
is
comparable
databases.
excretion
different
predicted
toxicity
comparable.
This
work
further
contributes
deeper
characterization
therapeutic
potential.
Science,
Journal Year:
2023,
Volume and Issue:
381(6654), P. 164 - 170
Published: July 13, 2023
Despite
advances
in
molecular
biology,
genetics,
computation,
and
medicinal
chemistry,
infectious
disease
remains
an
ominous
threat
to
public
health.
Addressing
the
challenges
posed
by
pathogen
outbreaks,
pandemics,
antimicrobial
resistance
will
require
concerted
interdisciplinary
efforts.
In
conjunction
with
systems
synthetic
artificial
intelligence
(AI)
is
now
leading
rapid
progress,
expanding
anti-infective
drug
discovery,
enhancing
our
understanding
of
infection
accelerating
development
diagnostics.
this
Review,
we
discuss
approaches
for
detecting,
treating,
diseases,
underscoring
progress
supported
AI
each
case.
We
suggest
future
applications
how
it
might
be
harnessed
help
control
outbreaks
pandemics.
Nature Reviews Drug Discovery,
Journal Year:
2023,
Volume and Issue:
22(4), P. 317 - 335
Published: Feb. 13, 2023
For
decades,
preclinical
toxicology
was
essentially
a
descriptive
discipline
in
which
treatment-related
effects
were
carefully
reported
and
used
as
basis
to
calculate
safety
margins
for
drug
candidates.
In
recent
years,
however,
technological
advances
have
increasingly
enabled
researchers
gain
insights
into
toxicity
mechanisms,
supporting
greater
understanding
of
species
relevance
translatability
humans,
prediction
events,
mitigation
side
development
biomarkers.
Consequently,
investigative
(or
mechanistic)
has
been
gaining
momentum
is
now
key
capability
the
pharmaceutical
industry.
Here,
we
provide
an
overview
current
status
field
using
case
studies
discuss
potential
impact
ongoing
developments,
based
on
survey
toxicologists
from
14
European-based
medium-sized
large
companies.
Investigative
tools
strategies
are
companies
reduce
safety-related
attrition
development.
This
Perspective
article
summarizes
goals
toxicology,
highlights
approaches
discusses
selected
emerging
technologies
that
improve
safety-testing
paradigm.
Discover Materials,
Journal Year:
2021,
Volume and Issue:
1(1)
Published: April 19, 2021
Herein,
we
review
aspects
of
leading-edge
research
and
innovation
in
materials
science
that
exploit
big
data
machine
learning
(ML),
two
computer
concepts
combine
to
yield
computational
intelligence.
ML
can
accelerate
the
solution
intricate
chemical
problems
even
solve
otherwise
would
not
be
tractable.
However,
potential
benefits
come
at
cost
production;
is,
algorithms
demand
large
volumes
various
natures
from
different
sources,
material
properties
sensor
data.
In
survey,
propose
a
roadmap
for
future
developments
with
emphasis
on
computer-aided
discovery
new
analysis
sensing
compounds,
both
prominent
fields
context
science.
addition
providing
an
overview
recent
advances,
elaborate
upon
conceptual
practical
limitations
applied
science,
outlining
processes,
discussing
pitfalls,
reviewing
cases
success
failure.
Journal of Chemical Information and Modeling,
Journal Year:
2023,
Volume and Issue:
63(9), P. 2628 - 2643
Published: April 26, 2023
Toxicity
prediction
is
a
critical
step
in
the
drug
discovery
process
that
helps
identify
and
prioritize
compounds
with
greatest
potential
for
safe
effective
use
humans,
while
also
reducing
risk
of
costly
late-stage
failures.
It
estimated
over
30%
candidates
are
discarded
owing
to
toxicity.
Recently,
artificial
intelligence
(AI)
has
been
used
improve
toxicity
as
it
provides
more
accurate
efficient
methods
identifying
potentially
toxic
effects
new
before
they
tested
human
clinical
trials,
thus
saving
time
money.
In
this
review,
we
present
an
overview
recent
advances
AI-based
prediction,
including
various
machine
learning
algorithms
deep
architectures,
six
major
properties
Tox21
assay
end
points.
Additionally,
provide
list
public
data
sources
useful
tools
research
community
highlight
challenges
must
be
addressed
enhance
model
performance.
Finally,
discuss
future
perspectives
prediction.
This
review
can
aid
researchers
understanding
pave
way
discovery.
Chemical Society Reviews,
Journal Year:
2022,
Volume and Issue:
51(15), P. 6475 - 6573
Published: Jan. 1, 2022
Machine
learning
(ML)
has
emerged
into
formidable
force
for
identifying
hidden
but
pertinent
patterns
within
a
given
data
set
with
the
objective
of
subsequent
generation
automated
predictive
behavior.
In
recent
years,
it
is
safe
to
conclude
that
ML
and
its
close
cousin
deep
(DL)
have
ushered
unprecedented
developments
in
all
areas
physical
sciences
especially
chemistry.
Not
only
classical
variants
,
even
those
trainable
on
near-term
quantum
hardwares
been
developed
promising
outcomes.
Such
algorithms
revolutionzed
material
design
performance
photo-voltaics,
electronic
structure
calculations
ground
excited
states
correlated
matter,
computation
force-fields
potential
energy
surfaces
informing
chemical
reaction
dynamics,
reactivity
inspired
rational
strategies
drug
designing
classification
phases
matter
accurate
identification
emergent
criticality.
this
review
we
shall
explicate
subset
such
topics
delineate
contributions
made
by
both
computing
enhanced
machine
over
past
few
years.
We
not
present
brief
overview
well-known
techniques
also
highlight
their
using
statistical
insight.
The
foster
exposition
aforesaid
empower
promote
cross-pollination
among
future-research
chemistry
which
can
benefit
from
turn
potentially
accelerate
growth
algorithms.
Advanced Functional Materials,
Journal Year:
2023,
Volume and Issue:
33(17)
Published: Feb. 15, 2023
Abstract
Data‐driven
epoch,
the
development
of
machine
learning
(ML)
in
materials
and
device
design
is
an
irreversible
trend.
Its
ability
efficiency
to
handle
nonlinear
game‐playing
problems
unmatched
by
traditional
simulation
computing
software
trial‐error
experiments.
Perovskite
solar
cells
are
complex
physicochemical
devices
(systems)
that
consist
perovskite
materials,
transport
layer
electrodes.
Predicting
properties
screening
component
related
strong
point
ML.
However,
applications
ML
has
only
begun
boom
last
two
years,
so
it
necessary
provide
a
review
involved
technologies,
application
status,
facing
urgent
challenges
blueprint.
The Annual Review of Pharmacology and Toxicology,
Journal Year:
2023,
Volume and Issue:
64(1), P. 527 - 550
Published: Sept. 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.
