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.
Digital Discovery,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 28, 2024
Data-driven
reaction
discovery
and
development
is
a
growing
field
that
relies
on
the
use
of
molecular
descriptors
to
capture
key
information
about
substrates,
ligands,
targets.
Broad
adaptation
this
strategy
hindered
by
associated
computational
cost
descriptor
calculation,
especially
when
considering
conformational
flexibility.
Descriptor
libraries
can
be
precomputed
agnostic
application
reduce
burden
data-driven
development.
However,
as
one
often
applies
these
models
evaluate
novel
hypothetical
structures,
it
would
ideal
predict
compounds
on-the-fly.
Herein,
we
report
DFT-level
for
ensembles
8528
carboxylic
acids
8172
alkyl
amines
towards
goal.
Employing
2D
3D
graph
neural
network
architectures
trained
culminated
in
predictive
molecule-level
descriptors,
well
bond-
atom-level
conserved
reactive
site
(carboxylic
acid
or
amine).
The
predictions
were
confirmed
robust
an
external
validation
set
medicinally-relevant
amines.
Additionally,
retrospective
study
correlating
rate
amide
coupling
reactions
demonstrated
suitability
predicted
downstream
applications.
Ultimately,
enable
high-fidelity
vast
number
potential
greatly
increasing
accessibility
Organic Process Research & Development,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 7, 2024
As
the
field
of
nonprecious
metal
catalysis
continues
to
expand,
we
pursue
a
review
series
covering
selected
transformations
in
this
area
over
short
time
interval
highlight
practical
advancements.
We
seek
raise
awareness
both
current
art
and
need
continue
development
toward
broader
applications
earth-abundant
metals
chemical
pharmaceutical
industries.
ACS Catalysis,
Год журнала:
2024,
Номер
15(2), С. 817 - 827
Опубликована: Дек. 27, 2024
Transition
metal
catalysis
is
crucial
for
the
synthesis
of
complex
molecules,
with
ligands
and
bases
playing
a
pivotal
role
in
optimizing
cross-coupling
reactions.
Despite
advancements
ligand
design
base
selection,
achieving
effective
synergy
between
these
components
remains
challenging.
We
present
here
general
approach
to
nickel-catalyzed
photoredox
reactions
employing
tert-butylamine
as
cost-effective
bifunctional
additive,
acting
ligand.
This
method
proves
C-O
C-N
bond-forming
diverse
array
nucleophiles,
including
phenols,
aliphatic
alcohols,
anilines,
sulfonamides,
sulfoximines,
imines.
Notably,
protocol
demonstrates
significant
applicability
biomolecule
derivatization
facilitates
sequential
one-pot
functionalizations.
Spectroscopic
investigations
revealed
robustness
dynamic
catalytic
system,
while
elucidation
structure-reactivity
relationships
demonstrated
how
computed
molecular
properties
both
nucleophile
electrophile
correlated
reaction
performance,
providing
foundation
outcome
prediction.
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.
