Journal of the American Chemical Society,
Год журнала:
2024,
Номер
146(5), С. 3458 - 3470
Опубликована: Янв. 25, 2024
Ligand
modulation
of
transition-metal
catalysts
to
achieve
optimal
reactivity
and
selectivity
in
alkene
hydrofunctionalization
is
a
fundamental
challenge
synthetic
organic
chemistry.
Hydroaminoalkylation,
an
atom-economical
approach
for
alkylating
amines
using
alkenes,
particularly
significant
amine
synthesis
the
pharmaceutical,
agrochemical,
fine
chemical
industries.
However,
existing
methods
usually
require
specific
substrate
combinations
precise
regio-
stereoselectivity,
which
limits
their
practical
utility.
Protocols
allowing
regiodivergent
hydroaminoalkylation
from
same
starting
materials,
controlling
both
regiochemical
stereochemical
outcomes,
are
currently
absent.
Herein,
we
report
ligand-controlled,
nickel-catalyzed
unactivated
alkenes
with
Communications Chemistry,
Год журнала:
2021,
Номер
4(1)
Опубликована: Авг. 2, 2021
Autonomous
process
optimization
involves
the
human
intervention-free
exploration
of
a
range
parameters
to
improve
responses
such
as
product
yield
and
selectivity.
Utilizing
off-the-shelf
components,
we
develop
closed-loop
system
for
carrying
out
parallel
autonomous
experiments
in
batch.
Upon
implementation
our
stereoselective
Suzuki-Miyaura
coupling,
find
that
definition
set
meaningful,
broad,
unbiased
is
most
critical
aspect
successful
optimization.
Importantly,
discern
phosphine
ligand,
categorical
parameter,
vital
determination
reaction
outcome.
To
date,
parameter
selection
has
relied
on
chemical
intuition,
potentially
introducing
bias
into
experimental
design.
In
seeking
systematic
method
selecting
diverse
ligands,
strategy
leverages
computed
molecular
feature
clustering.
The
resulting
uncovers
conditions
selectively
access
desired
isomer
high
yield.
Journal of the American Chemical Society,
Год журнала:
2022,
Номер
145(1), С. 110 - 121
Опубликована: Дек. 27, 2022
Optimization
of
the
catalyst
structure
to
simultaneously
improve
multiple
reaction
objectives
(e.g.,
yield,
enantioselectivity,
and
regioselectivity)
remains
a
formidable
challenge.
Herein,
we
describe
machine
learning
workflow
for
multi-objective
optimization
catalytic
reactions
that
employ
chiral
bisphosphine
ligands.
This
was
demonstrated
through
two
sequential
required
in
asymmetric
synthesis
an
active
pharmaceutical
ingredient.
To
accomplish
this,
density
functional
theory-derived
database
>550
ligands
constructed,
designer
chemical
space
mapping
technique
established.
The
protocol
used
classification
methods
identify
catalysts,
followed
by
linear
regression
model
selectivity.
led
prediction
validation
significantly
improved
all
outputs,
suggesting
general
strategy
can
be
readily
implemented
optimizations
where
performance
is
controlled
Chemical Science,
Год журнала:
2023,
Номер
14(19), С. 4997 - 5005
Опубликована: Янв. 1, 2023
The
lack
of
publicly
available,
large,
and
unbiased
datasets
is
a
key
bottleneck
for
the
application
machine
learning
(ML)
methods
in
synthetic
chemistry.
Data
from
electronic
laboratory
notebooks
(ELNs)
could
provide
less
biased,
large
datasets,
but
no
such
have
been
made
available.
first
real-world
dataset
ELNs
pharmaceutical
company
disclosed
its
relationship
to
high-throughput
experimentation
(HTE)
described.
For
chemical
yield
predictions,
task
synthesis,
an
attributed
graph
neural
network
(AGNN)
performs
as
well
or
better
than
best
previous
models
on
two
HTE
Suzuki-Miyaura
Buchwald-Hartwig
reactions.
However,
training
AGNN
ELN
does
not
lead
predictive
model.
implications
using
data
ML-based
are
discussed
context
predictions.
Journal of the American Chemical Society,
Год журнала:
2023,
Номер
145(12), С. 6596 - 6614
Опубликована: Март 13, 2023
The
use
of
two
or
more
metal
catalysts
in
a
reaction
is
powerful
synthetic
strategy
to
access
complex
targets
efficiently
and
selectively
from
simple
starting
materials.
While
capable
uniting
distinct
reactivities,
the
principles
governing
multimetallic
catalysis
are
not
always
intuitive,
making
discovery
optimization
new
reactions
challenging.
Here,
we
outline
our
perspective
on
design
elements
using
precedent
well-documented
C–C
bond-forming
reactions.
These
strategies
provide
insight
into
synergy
compatibility
individual
components
reaction.
Advantages
limitations
discussed
promote
further
development
field.
Digital Discovery,
Год журнала:
2023,
Номер
3(1), С. 23 - 33
Опубликована: Дек. 6, 2023
The
ASLLA
Symposium
focused
on
accelerating
chemical
science
with
AI.
Discussions
data,
new
applications,
algorithms,
and
education
were
summarized.
Recommendations
for
researchers,
educators,
academic
bodies
provided.
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.
