ChemMedChem,
Journal Year:
2024,
Volume and Issue:
19(21)
Published: July 18, 2024
Abstract
Carbon
dioxide
(CO
2
)
is
an
economically
viable
and
abundant
carbon
source
that
can
be
incorporated
into
compounds
such
as
C2‐carboxylated
1,3‐azoles
relevant
to
the
pharmaceutical,
cosmetics,
pesticide
industries.
Of
2.4
million
commercially
available
C2‐unsubstituted
1,3‐azole
compounds,
less
than
1
%
are
currently
purchasable
their
derivatives,
highlighting
substantial
gap
in
compound
availability.
This
availability
leaves
ample
opportunities
for
exploring
synthetic
accessibility
use
of
carboxylated
azoles
bioactive
compounds.
In
this
study,
we
analyze
quantify
relevance
small‐molecule
research.
An
analysis
molecular
databases
ZINC,
ChEMBL,
COSMOS,
DrugBank
identified
anticoagulant
aroma‐giving
Moreover,
a
pharmacophore
highlights
promising
pharmaceutical
potential
associated
with
1,3‐azoles,
revealing
ATP‐sensitive
inward
rectifier
potassium
channel
(K
ATP
Kinesin‐like
protein
KIF18
A
targets
potentially
addressed
1,3‐azoles.
several
bioisosteres
conclusion,
further
exploration
chemical
space
recommended
harness
full
drug
discovery
related
fields.
Journal of the American Chemical Society,
Journal Year:
2022,
Volume and Issue:
144(49), P. 22599 - 22610
Published: Dec. 2, 2022
The
molecular
structures
synthesizable
by
organic
chemists
dictate
the
functions
they
can
create.
invention
and
development
of
chemical
reactions
are
thus
critical
for
to
access
new
desirable
functional
molecules
in
all
disciplines
chemistry.
This
work
seeks
expedite
exploration
emerging
areas
chemistry
devising
a
machine-learning-guided
workflow
reaction
discovery.
Specifically,
this
study
uses
machine
learning
predict
competent
electrochemical
reactions.
To
end,
we
first
develop
representation
that
enables
production
general
models
with
limited
training
data.
Next,
employ
automated
experimentation
test
large
number
These
categorized
as
or
incompetent
mixtures,
classification
model
was
trained
competency.
is
used
screen
38,865
potential
silico,
predictions
identify
synthetic
mechanistic
interest,
80%
which
found
be
competent.
Additionally,
provide
38,865-member
set
hope
accelerating
field.
We
envision
adopting
such
could
enable
rapid
many
fields
Chemical Communications,
Journal Year:
2023,
Volume and Issue:
59(49), P. 7483 - 7505
Published: Jan. 1, 2023
Copper-catalyzed
decarboxylative
reactions
are
powerful
strategies
for
the
construction
of
widely
available
skeletons
such
as
allenes,
ethynyl-containing
heterocycles,
and
quaternary
carbon
centers.
Digital Discovery,
Journal Year:
2023,
Volume and Issue:
2(2), P. 356 - 367
Published: Jan. 1, 2023
A
generic
framework
for
enhancing
an
initial
solubility
prediction
with
ML,
even
simple
methods
and
a
modestly
sized,
sparse
dataset.
We
dissect
the
setup
to
show
model
“locking
on”
target
system
as
more
data
are
made
available.
Journal of the American Chemical Society,
Journal Year:
2023,
Volume and Issue:
145(23), P. 12771 - 12782
Published: May 30, 2023
A
bifunctional
iminophosphorane
(BIMP)-catalyzed,
enantioselective
intramolecular
oxa-Michael
reaction
of
alcohols
to
tethered,
low
electrophilicity
Michael
acceptors
is
described.
Improved
reactivity
over
previous
reports
(1
day
vs
7
days),
excellent
yields
(up
99%),
and
enantiomeric
ratios
99.5:0.5
er)
are
demonstrated.
The
broad
scope,
enabled
by
catalyst
modularity
tunability,
includes
substituted
tetrahydrofurans
(THFs)
tetrahydropyrans
(THPs),
oxaspirocycles,
sugar
natural
product
derivatives,
dihydro-(iso)-benzofurans,
iso-chromans.
state-of-the-art
computational
study
revealed
that
the
enantioselectivity
originates
from
presence
several
favorable
intermolecular
hydrogen
bonds
between
BIMP
substrate
induce
stabilizing
electrostatic
orbital
interactions.
newly
developed
catalytic
approach
was
carried
out
on
multigram
scale,
multiple
adducts
were
further
derivatized
an
array
useful
building
blocks,
providing
access
enantioenriched
biologically
active
molecules
products.
Proceedings of the National Academy of Sciences,
Journal Year:
2022,
Volume and Issue:
119(16)
Published: April 11, 2022
Significance
Given
the
ubiquity
of
amide
coupling
reactions,
understanding
factors
which
influence
success
reaction
and
having
means
to
predict
rate
would
streamline
synthetic
efforts.
This
study
outlines
a
data
science–based
workflow
for
effective
statistical
modeling
with
sparse
experimental
data.
We
demonstrated
informed
substrate
selection,
collection
interpretable
molecular
descriptors,
model
development
rates.
The
resulting
models
illuminate
features
that
impact
allow
prediction
untested
Chemical Science,
Journal Year:
2022,
Volume and Issue:
13(43), P. 12681 - 12695
Published: Jan. 1, 2022
A
model
for
S
N
Ar
reactivity
is
reported,
built
from
relative
rate
data
obtained
by
competition
studies.
Based
only
on
molecular
descriptors
of
the
electrophile,
predicts
and
site
selectivity
many
complex
substrates.
ChemPhysChem,
Journal Year:
2023,
Volume and Issue:
24(14)
Published: May 3, 2023
Nucleophilicity
and
electrophilicity
dictate
the
reactivity
of
polar
organic
reactions.
In
past
decades,
Mayr
et
al.
established
a
quantitative
scale
for
nucleophilicity
(N)
(E),
which
proved
to
be
useful
tool
rationalization
chemical
reactivity.
this
study,
holistic
prediction
model
was
developed
through
machine-learning
approach.
rSPOC,
an
ensemble
molecular
representation
with
structural,
physicochemical
solvent
features,
purpose.
With
1115
nucleophiles,
285
electrophiles,
22
solvents,
dataset
is
currently
largest
one
prediction.
The
rSPOC
trained
Extra
Trees
algorithm
showed
high
accuracy
in
predicting
Mayr's
N
E
parameters
R2
0.92
0.93,
MAE
1.45
1.45,
respectively.
Furthermore,
practical
applications
model,
instance,
NADH,
NADPH
series
enamines
potential
molecules
unknown
within
seconds.
An
online
platform
(http://isyn.luoszgroup.com/)
constructed
based
on
current
available
free
scientific
community.
Journal of the American Chemical Society,
Journal Year:
2023,
Volume and Issue:
145(39), P. 21587 - 21599
Published: Sept. 21, 2023
In
catalysis,
linear
free
energy
relationships
(LFERs)
are
commonly
used
to
identify
reaction
descriptors
that
enable
the
prediction
of
outcomes
and
design
more
effective
catalysts.
Herein,
LFERs
established
for
reductive
cleavage
C(sp3)-X
bond
in
alkyl
halides
(RX)
by
Cu
complexes.
This
represents
activation
step
atom
transfer
radical
polymerization
addition/cyclization.
The
values
rate
constant,
kact,
107
complex/RX
couples
5
different
solvents
spanning
over
13
orders
magnitude
were
effectively
interpolated
equation:
log
kact
=
sC(I
+
C
S),
where
I,
C,
S
are,
respectively,
initiator,
catalyst,
solvent
parameters,
sC
is
catalyst-specific
sensitivity
parameter.
Furthermore,
each
these
parameters
was
correlated
relevant
descriptors,
which
included
dissociation
RX
its
Tolman
cone
angle
θ,
electron
affinity
X,
stabilization
energy,
standard
reduction
potential
complex,
polarizability
parameter
π*
solvent,
distortion
complex
transition
state.
set
establishes
fundamental
properties
complexes
determine
their
reactivity
need
be
considered
when
designing
novel
systems
reactions.
Finally,
a
multivariate
regression
(MLR)
approach
adopted
develop
an
objective
model
surpassed
predictive
capability
LFER
equation.
Thus,
MLR
employed
predict
>2000
pairs.