The Chemical Record,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 5, 2024
Abstract
Advancements
in
synthetic
organic
chemistry
are
closely
related
to
understanding
substrate
and
catalyst
reactivities
through
detailed
mechanistic
studies.
Traditional
investigations
labor‐intensive
rely
on
experimental
kinetic,
thermodynamic,
spectroscopic
data.
Linear
free
energy
relationships
(LFERs),
exemplified
by
Hammett
relationships,
have
long
facilitated
reactivity
prediction
despite
their
inherent
limitations
when
using
constants
or
incorporating
comprehensive
Data‐driven
modeling,
which
integrates
cheminformatics
with
machine
learning,
offers
powerful
tools
for
predicting
interpreting
mechanisms
effectively
handling
complex
multiparameter
strategies.
This
review
explores
selected
examples
of
data‐driven
strategies
investigating
reaction
mechanisms.
It
highlights
the
evolution
application
computational
descriptors
inference.
The
application
of
statistical
modeling
in
organic
chemistry
is
emerging
as
a
standard
practice
for
probing
structure-activity
relationships
and
predictive
tool
many
optimization
objectives.
This
review
aimed
tutorial
those
entering
the
area
chemistry.
We
provide
case
studies
to
highlight
considerations
approaches
that
can
be
used
successfully
analyze
datasets
low
data
regimes,
common
situation
encountered
given
experimental
demands
Statistical
hinges
on
(what
being
modeled),
descriptors
(how
are
represented),
algorithms
modeled).
Herein,
we
focus
how
various
reaction
outputs
(e.g.,
yield,
rate,
selectivity,
solubility,
stability,
turnover
number)
structures
binned,
heavily
skewed,
distributed)
influence
choice
algorithm
constructing
chemically
insightful
models.
Chemical Science,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
Label
ranking
is
introduced
as
a
conceptually
new
means
for
prioritizing
experiments.
Their
simplicity,
ease
of
application,
and
the
use
aggregation
facilitate
their
ability
to
make
accurate
predictions
with
small
datasets.
Journal of the American Chemical Society,
Год журнала:
2024,
Номер
146(19), С. 13266 - 13275
Опубликована: Май 2, 2024
Due
to
the
magnitude
of
chemical
space,
discovery
novel
substrates
in
energy
transfer
(EnT)
catalysis
remains
a
daunting
task.
Experimental
and
computational
strategies
identify
compounds
that
successfully
undergo
EnT-mediated
reactions
are
limited
by
their
time
cost
efficiency.
To
accelerate
process
EnT
catalysis,
we
herein
present
EnTdecker
platform,
which
facilitates
large-scale
virtual
screening
potential
using
machine-learning
(ML)
based
predictions
excited
state
properties.
achieve
this,
data
set
is
created
containing
more
than
34,000
molecules
aiming
cover
vast
fraction
synthetically
relevant
compound
space
for
catalysis.
Using
this
predictive
models
trained,
aptitude
an
in-lab
application
demonstrated
rediscovering
successful
from
literature
as
well
experimental
validation
through
luminescence-based
screening.
By
reducing
effort
needed
obtain
properties,
platform
represents
tool
efficiently
guide
substrate
selection
increase
success
rate
Moreover,
easy-to-use
web
application,
made
publicly
accessible
under
entdecker.uni-muenster.de.
European Journal of Organic Chemistry,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 11, 2024
Abstract
A
key
challenge
in
synthetic
chemistry
is
the
selection
of
high‐performing
ligands
for
cross‐coupling
reactions.
To
address
this
challenge,
work
presents
a
classification
workflow
to
identify
physicochemical
descriptors
that
bin
monophosphine
as
active
or
inactive
Ni‐catalyzed
Suzuki‐Miyaura
coupling
Using
five
previously
published
high‐throughput
experimentation
datasets
training,
we
found
binary
classifier
using
phosphine's
minimum
buried
volume
and
Boltzmann‐averaged
electrostatic
potential
most
effective
at
distinguishing
high
low‐yielding
ligands.
Experimental
validations
are
also
presented.
two
from
represent
chemical
space
leads
more
predictive
guide
structure‐reactivity
relationships
compared
with
classic
representations.
Angewandte Chemie International Edition,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 2, 2025
The
azidofunctionalization
of
alkenes
under
mild
conditions
using
commercially
available
starting
materials
and
easily
accessible
reagents
is
reported
based
on
a
radical-polar
crossover
strategy.
A
broad
range
alkenes,
including
vinyl
arenes,
enamides,
enol
ethers,
sulfides,
dehydroamino
esters,
were
regioselectively
functionalized
with
an
azide
nucleophiles
such
as
azoles,
carboxylic
acids,
alcohols,
phosphoric
oximes,
phenols.
method
led
to
more
efficient
synthesis
1,2-azidofunctionalized
pharmaceutical
intermediates
when
compared
previous
approaches,
resulting
in
both
reduction
step
count
increase
overall
yield.
scope
limitations
these
transformations
further
investigated
through
standard
unbiased
selection
15
substrate
combinations
out
1,175,658
possible
clustering
technique.
Angewandte Chemie,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 2, 2025
Abstract
The
azidofunctionalization
of
alkenes
under
mild
conditions
using
commercially
available
starting
materials
and
easily
accessible
reagents
is
reported
based
on
a
radical‐polar
crossover
strategy.
A
broad
range
alkenes,
including
vinyl
arenes,
enamides,
enol
ethers,
sulfides,
dehydroamino
esters,
were
regioselectively
functionalized
with
an
azide
nucleophiles
such
as
azoles,
carboxylic
acids,
alcohols,
phosphoric
oximes,
phenols.
method
led
to
more
efficient
synthesis
1,2‐azidofunctionalized
pharmaceutical
intermediates
when
compared
previous
approaches,
resulting
in
both
reduction
step
count
increase
overall
yield.
scope
limitations
these
transformations
further
investigated
through
standard
unbiased
selection
15
substrate
combinations
out
1,175,658
possible
clustering
technique.
