npj Systems Biology and Applications,
Journal Year:
2022,
Volume and Issue:
8(1)
Published: Feb. 23, 2022
Prediction
algorithms
for
protein
or
gene
structures,
including
transcription
factor
binding
from
sequence
information,
have
been
transformative
in
understanding
regulation.
Here
we
ask
whether
human
transcriptomic
profiles
can
be
predicted
solely
the
expression
of
factors
(TFs).
We
find
that
1600
TFs
explain
>95%
variance
25,000
genes.
Using
light-up
technique
to
inspect
trained
NN,
an
over-representation
known
TF-gene
regulations.
Furthermore,
learned
prediction
network
has
a
hierarchical
organization.
A
smaller
set
around
125
core
could
close
80%
variance.
Interestingly,
reducing
number
below
500
induces
rapid
decline
performance.
Next,
evaluated
model
using
transcriptional
data
22
diseases.
The
were
sufficient
predict
dysregulation
target
genes
(rho
=
0.61,
P
<
10-216).
By
inspecting
model,
key
causative
extracted
subsequent
validation
disease-associated
genetic
variants.
demonstrate
methodology
constructing
interpretable
neural
predictor,
where
analyses
predictors
identified
inducing
changes
during
disease.
Cell Discovery,
Journal Year:
2020,
Volume and Issue:
6(1)
Published: March 16, 2020
Abstract
Human
coronaviruses
(HCoVs),
including
severe
acute
respiratory
syndrome
coronavirus
(SARS-CoV)
and
2019
novel
(2019-nCoV,
also
known
as
SARS-CoV-2),
lead
global
epidemics
with
high
morbidity
mortality.
However,
there
are
currently
no
effective
drugs
targeting
2019-nCoV/SARS-CoV-2.
Drug
repurposing,
representing
an
drug
discovery
strategy
from
existing
drugs,
could
shorten
the
time
reduce
cost
compared
to
de
novo
discovery.
In
this
study,
we
present
integrative,
antiviral
repurposing
methodology
implementing
a
systems
pharmacology-based
network
medicine
platform,
quantifying
interplay
between
HCoV–host
interactome
targets
in
human
protein–protein
interaction
network.
Phylogenetic
analyses
of
15
HCoV
whole
genomes
reveal
that
2019-nCoV/SARS-CoV-2
shares
highest
nucleotide
sequence
identity
SARS-CoV
(79.7%).
Specifically,
envelope
nucleocapsid
proteins
two
evolutionarily
conserved
regions,
having
identities
96%
89.6%,
respectively,
SARS-CoV.
Using
proximity
interactions
interactome,
prioritize
16
potential
anti-HCoV
repurposable
(e.g.,
melatonin,
mercaptopurine,
sirolimus)
further
validated
by
enrichment
drug-gene
signatures
HCoV-induced
transcriptomics
data
cell
lines.
We
identify
three
combinations
sirolimus
plus
dactinomycin,
mercaptopurine
toremifene
emodin)
captured
“
Complementary
Exposure
”
pattern:
both
hit
subnetwork,
but
target
separate
neighborhoods
summary,
study
offers
powerful
network-based
methodologies
for
rapid
identification
candidate
International Journal of Molecular Sciences,
Journal Year:
2020,
Volume and Issue:
21(11), P. 3922 - 3922
Published: May 30, 2020
The
novel
coronavirus,
COVID-19,
caused
by
SARS-CoV-2,
is
a
global
health
pandemic
that
started
in
December
2019.
effective
drug
target
among
coronaviruses
the
main
protease
Mpro,
because
of
its
essential
role
processing
polyproteins
are
translated
from
viral
RNA.
In
this
study,
bioactivity
some
selected
heterocyclic
drugs
named
Favipiravir
(1),
Amodiaquine
(2),
2'-Fluoro-2'-deoxycytidine
(3),
and
Ribavirin
(4)
was
evaluated
as
inhibitors
nucleotide
analogues
for
COVID-19
using
computational
modeling
strategies.
density
functional
theory
(DFT)
calculations
were
performed
to
estimate
thermal
parameters,
dipole
moment,
polarizability,
molecular
electrostatic
potential
present
drugs;
additionally,
Mulliken
atomic
charges
well
chemical
reactivity
descriptors
investigated.
nominated
docked
on
SARS-CoV-2
(PDB:
6LU7)
evaluate
binding
affinity
these
drugs.
Besides,
computations
data
DFT
docking
simulation
studies
predicted
(2)
has
least
energy
(-7.77
Kcal/mol)
might
serve
good
inhibitor
comparable
with
approved
medicines,
hydroxychloroquine,
remdesivir
which
have
-6.06
-4.96
Kcal/mol,
respectively.
high
2
attributed
presence
three
hydrogen
bonds
along
different
hydrophobic
interactions
between
critical
amino
acids
residues
receptor.
Finally,
estimated
results
used
illustrate
findings.
showed
highest
lying
HOMO,
electrophilicity
index,
basicity,
moment.
All
parameters
could
share
extent
significantly
affect
active
protein
sites.
PLoS Biology,
Journal Year:
2020,
Volume and Issue:
18(11), P. e3000970 - e3000970
Published: Nov. 6, 2020
The
global
coronavirus
disease
2019
(COVID-19)
pandemic,
caused
by
severe
acute
respiratory
syndrome
2
(SARS-CoV-2),
has
led
to
unprecedented
social
and
economic
consequences.
risk
of
morbidity
mortality
due
COVID-19
increases
dramatically
in
the
presence
coexisting
medical
conditions,
while
underlying
mechanisms
remain
unclear.
Furthermore,
there
are
no
approved
therapies
for
COVID-19.
This
study
aims
identify
SARS-CoV-2
pathogenesis,
manifestations,
using
network
medicine
methodologies
along
with
clinical
multi-omics
observations.
We
incorporate
virus–host
protein–protein
interactions,
transcriptomics,
proteomics
into
human
interactome.
Network
proximity
measurement
revealed
pathogenesis
broad
COVID-19-associated
manifestations.
Analyses
single-cell
RNA
sequencing
data
show
that
co-expression
ACE2
TMPRSS2
is
elevated
absorptive
enterocytes
from
inflamed
ileal
tissues
Crohn
patients
compared
uninflamed
tissues,
revealing
shared
pathobiology
between
inflammatory
bowel
disease.
Integrative
analyses
metabolomics
transcriptomics
(bulk
single-cell)
asthma
indicate
shares
an
intermediate
molecular
profile
(including
IRAK3
ADRB2
).
To
prioritize
potential
treatments,
we
combined
network-based
prediction
a
propensity
score
(PS)
matching
observational
26,779
individuals
registry.
identified
melatonin
usage
(odds
ratio
[OR]
=
0.72,
95%
CI
0.56–0.91)
significantly
associated
28%
reduced
likelihood
positive
laboratory
test
result
confirmed
reverse
transcription–polymerase
chain
reaction
assay.
Using
PS
user
active
comparator
design,
determined
was
use
angiotensin
II
receptor
blockers
(OR
0.70,
0.54–0.92)
or
angiotensin-converting
enzyme
inhibitors
0.69,
0.52–0.90).
Importantly,
0.48,
0.31–0.75)
52%
African
Americans
after
adjusting
age,
sex,
race,
smoking
history,
various
comorbidities
matching.
In
summary,
this
presents
integrative
platform
predicting
manifestations
identifying
prevention
treatment
Molecules,
Journal Year:
2022,
Volume and Issue:
27(13), P. 4060 - 4060
Published: June 24, 2022
Ethnopharmacology,
through
the
description
of
beneficial
effects
plants,
has
provided
an
early
framework
for
therapeutic
use
natural
compounds.
Natural
products,
either
in
their
native
form
or
after
crude
extraction
active
ingredients,
have
long
been
used
by
different
populations
and
explored
as
invaluable
sources
drug
design.
The
transition
from
traditional
ethnopharmacology
to
discovery
followed
a
straightforward
path,
assisted
evolution
isolation
characterization
methods,
increase
computational
power,
development
specific
chemoinformatic
methods.
deriving
extensive
exploitation
product
chemical
space
led
novel
compounds
with
pharmaceutical
properties,
although
this
was
not
analogous
drugs.
In
work,
we
discuss
ideas
silico
discovery,
applied
products.
We
point
out
that,
past,
starting
plant
itself,
identified
sustained
ethnopharmacological
research,
compound
analysis
testing.
contrast,
recent
years,
substance
pinpointed
methods
(in
docking
molecular
dynamics,
network
pharmacology),
identification
plant(s)
containing
ingredient,
existing
putative
information.
further
stress
potential
pitfalls
absolute
need
vitro
vivo
validation
requirement.
Finally,
present
our
contribution
products'
discussing
examples,
applying
whole
continuum
rapidly
evolving
field.
detail,
report
antiviral
compounds,
based
on
products
against
influenza
SARS-CoV-2
substances
GPCR,
OXER1.
British Journal of Pharmacology,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 22, 2025
Abstract
Drug
discovery
is
a
complicated
process
through
which
new
therapeutics
are
identified
to
prevent
and
treat
specific
diseases.
Identification
of
drug–target
interactions
(DTIs)
stands
as
pivotal
aspect
within
the
realm
drug
development.
The
traditional
discovery,
especially
identification
DTIs,
marked
by
its
high
costs
experimental
assays
low
success
rates.
Computational
methods
have
emerged
indispensable
tools,
those
employing
artificial
intelligence
(AI)
methods,
could
streamline
process,
thereby
reducing
time
consumption
potentially
increasing
In
this
review,
we
focus
on
application
AI
techniques
in
DTI
prediction.
Specifically,
commence
with
comprehensive
overview
development,
along
systematic
prediction
validation
DTIs.
We
proceed
highlight
prominent
databases
toolkits
used
developing
for
prediction,
well
methodologies
evaluating
their
efficacy.
further
extend
exploration
into
three
primary
types
state‐of‐the‐art
including
classical
machine
learning,
deep
learning
network‐based
methods.
Finally,
summarize
key
findings
outline
current
challenges
future
directions
that
face
scientific
Acta Pharmaceutica Sinica B,
Journal Year:
2021,
Volume and Issue:
11(6), P. 1379 - 1399
Published: March 21, 2021
Over
the
past
decade,
traditional
Chinese
medicine
(TCM)
has
widely
embraced
systems
biology
and
its
various
data
integration
approaches
to
promote
modernization.
Thus,
integrative
pharmacology-based
(TCMIP)
was
proposed
as
a
paradigm
shift
in
TCM.
This
review
focuses
on
presentation
of
this
novel
concept
main
research
contents,
methodologies
applications
TCMIP.
First,
TCMIP
is
an
interdisciplinary
science
that
can
establish
qualitative
quantitative
pharmacokinetics–pharmacodynamics
(PK–PD)
correlations
through
knowledge
from
multiple
disciplines
techniques
different
PK–PD
processes
vivo.
Then,
contents
are
introduced
follows:
chemical
ADME/PK
profiles
TCM
formulas;
confirming
three
forms
active
substances
action
modes;
establishing
correlation;
building
correlations,
etc.
After
that,
we
summarize
existing
resources,
computational
models
experimental
methods
highlight
urgent
establishment
mathematical
modeling
methods.
Finally,
further
discuss
for
improvement
quality
control,
clarification
molecular
mechanisms
underlying
actions
TCMs
discovery
potential
new
drugs,
especially
TCM-related
combination
drug
discovery.
