Eccentric indices based QSPR evaluation of drugs for schizophrenia treatment
Heliyon,
Год журнала:
2025,
Номер
11(2), С. e42222 - e42222
Опубликована: Янв. 1, 2025
Schizophrenia
is
a
long-term,
serious
mental
health
condition
that
affects
how
person
thinks,
perceives,
and
behaves.
This
disorder
often
results
in
substantial
difficulties
social
interactions
work
performance.
Individuals
with
schizophrenia
might
appear
disconnected
from
reality,
causing
significant
distress
both
for
themselves
their
Friends.
Although
symptoms
of
can
vary
to
person,
they
typically
fall
into
three
main
categories:
cognitive,
negative,
psychotic.
Creating
computational
tools
find
develop
drugs
has
more
interest
the
past
few
years.
Regardless
developments
drug
design,
fundamental
approach
still
uses
topological
descriptors.
Topological
indices
are
used
estimate
bioactivity
chemical
compounds
QSAR/QSPR
studies.
In
general,
use
quantitative
structure-property
relationship
(QSPR),
numerical
values
connected
structures
predict
reactivity,
stability,
properties.
focuses
on
calculating
different
eccentric
(EIs),
developing
regression
model
thirteen
anti-schizophrenia
drugs,
applying
statistical
methods
establish
linear
between
QSPR
correlating
properties
indices.
Statistical
analysis
shows
p-values
less
than
equals
0.05,
f-test
value
(>2.5)
,
correlation
r
greater
0.7
validate
calculations.
The
coefficient
(r2)
convenient
tool
evaluating
models'
quality.
r2>0.7
essential
good
model.
show
significance
results,
while
accuracy
results.
order
fit
models
calculated
index
values,
eight
physicochemical
examined.
Drug
like
molar
refractivity
(cm3),
refractive
enthalpy
(kJ/mol),
melting,
boiling
flash
points
(°C),
complexity,
molecular
weight
all
effectively
estimated
by
By
examining
actual
verified.
Язык: Английский
Mechanistic study of α-mangostin derivatives as potent α-glucosidase inhibitors
Molecular Diversity,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 25, 2025
Язык: Английский
Predicting the anticancer activity of indole derivatives: A novel GP-tree-based QSAR model optimized by ALO with insights from molecular docking and decision-making methods
Computers in Biology and Medicine,
Год журнала:
2025,
Номер
189, С. 109988 - 109988
Опубликована: Март 9, 2025
Язык: Английский
Graph-Theoretic and Computational Analysis of QSAR Molecular Descriptors for Single Chain Diamond Silicates
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Март 28, 2025
Abstract
This
study
presents
a
comprehensive
graph-theoretic
and
computational
analysis
of
Quantitative
Structure-Activity
Relationship
(QSAR)
molecular
descriptors
for
Single
Chain
Diamond
Silicates
(CSn),
crucial
class
silicate
structures
defined
by
their
unique
connectivity
SiO₄
tetrahedra.
Various
descriptors,
including
the
Atom
Bond
Connectivity
(ABC)
Index,
Sum
(ABS)
Augmented
Zagreb
Index
(AZI),
(SZI),
Geometric
Arithmetic
(GAI),
(AGI),
are
examined
to
assess
structural,
electronic,
thermodynamic
properties.
Through
mathematical
formulations
modelling,
this
quantifies
complexity,
stability,
patterns
CSn,
enhancing
predictive
capabilities
QSAR
models.
The
findings
underscore
significance
in
characterising
networks,
with
applications
spanning
materials
science,
catalysis,
geochemistry.
Язык: Английский
Artificial intelligence for diabetes management – a review
Shivangi Maheshwari,
Anchin Kalia,
Jay Tewari
и другие.
Journal of Diabetes Metabolic Disorders & Control,
Год журнала:
2025,
Номер
12(1), С. 24 - 32
Опубликована: Янв. 1, 2025
Artificial
Intelligence
(AI)
driven
algorithms,
including
machine
learning
(ML)
and
deep
(DL),
analyze
vast
datasets
from
electronic
health
records
(EHRs),
wearable
sensors,
continuous
glucose
monitors
(CGMs)
to
provide
accurate
predictions
real-time
insights.
AI
applications
in
diabetes
management
include
automated
insulin
delivery
systems
(artificial
pancreas),
clinical
decision
support
(CDSS),
dietary
lifestyle
coaching,
telemedicine
platforms.
These
innovations
improve
glycemic
control,
reduce
complications,
empower
patients
with
personalized
treatment
plans.
care
faces
challenges
such
as
data
privacy
concerns,
lack
of
standardization,
physician
trust
issues,
regulatory
constraints.
Additionally,
models
often
suffer
bias
due
non-representative
datasets,
limiting
their
generalizability
across
diverse
populations.
Future
advancements
will
focus
on
improving
transparency
explainability,
enabling
better
integration
adoption.
As
continues
evolve,
its
into
holds
immense
potential
enhance
patient
outcomes,
healthcare
burdens,
pave
the
way
for
a
more
efficient,
personalized,
data-driven
approach
care.
Язык: Английский
Artificial Intelligence–Driven Computational Approaches in the Development of Anticancer Drugs
Cancers,
Год журнала:
2024,
Номер
16(22), С. 3884 - 3884
Опубликована: Ноя. 20, 2024
The
integration
of
AI
has
revolutionized
cancer
drug
development,
transforming
the
landscape
discovery
through
sophisticated
computational
techniques.
AI-powered
models
and
algorithms
have
enhanced
computer-aided
design
(CADD),
offering
unprecedented
precision
in
identifying
potential
anticancer
compounds.
Traditionally,
been
a
complex,
resource-intensive
process,
but
introduces
new
opportunities
to
accelerate
discovery,
reduce
costs,
optimize
efficiency.
This
manuscript
delves
into
transformative
applications
AI-driven
methodologies
predicting
developing
drugs,
critically
evaluating
their
reshape
future
therapeutics
while
addressing
challenges
limitations.
Язык: Английский
Genetic function algorithm (GFA) based QSAR, Molecular Design, and ADMET Screening to assess the antimalarial potential of Amodiaquine derivatives
The Microbe,
Год журнала:
2024,
Номер
unknown, С. 100208 - 100208
Опубликована: Ноя. 1, 2024
Язык: Английский
Current Status and Perspectives of Novel Radiopharmaceuticals with Heterologous Dual-targeted Functions: 2013–2023
Journal of Medicinal Chemistry,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 8, 2024
Radiotracers
provide
molecular-
and
cellular-level
information
in
a
noninvasive
manner
have
become
important
tools
for
precision
medicine.
In
particular,
the
successful
clinical
application
of
radioligand
therapeutic
(RLT)
has
further
strengthened
role
nuclear
medicine
treatment.
The
complicated
microenvironment
lesion
rendered
traditional
single-targeted
radiopharmaceuticals
incapable
fully
meeting
requirements.
design
development
dual-targeted
multitargeted
rapidly
emerged.
recent
years,
significant
progress
been
made
heterologous
radiopharmaceuticals.
This
perspective
aims
to
comprehensive
overview
these
radiopharmaceuticals,
with
special
focus
on
ligand
structures,
pharmacological
properties,
preclinical
evaluation.
Furthermore,
future
directions
are
discussed
from
this
perspective.
Язык: Английский