Journal of Chemical Information and Modeling,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 28, 2024
Methods
that
accelerate
the
evaluation
of
molecular
properties
are
essential
for
chemical
discovery.
While
some
degree
ligand
additivity
has
been
established
transition
metal
complexes,
it
is
underutilized
in
asymmetric
such
as
square
pyramidal
coordination
geometries
highly
relevant
to
catalysis.
To
develop
predictive
methods
beyond
simple
additivity,
we
apply
a
many-body
expansion
octahedral
and
complexes
introduce
correction
based
on
adjacent
ligands
(i.e.,
cis
interaction
model).
We
first
test
model
adiabatic
spin-splitting
energies
Fe(II)
predicting
DFT-calculated
values
unseen
binary
within
an
average
error
1.4
kcal/mol.
Uncertainty
analysis
reveals
optimal
basis,
comprising
homoleptic
mer
symmetric
complexes.
next
show
solved
basis)
infers
both
DFT-
CCSD(T)-calculated
catalytic
reaction
1
kcal/mol
average.
The
predicts
low-symmetry
with
outside
range
complex
energies.
observe
trans
interactions
unnecessary
most
monodentate
systems
but
can
be
important
combinations
ligands,
containing
mixture
bidentate
ligands.
Finally,
demonstrate
may
combined
Δ-learning
predict
CCSD(T)
from
exhaustively
calculated
DFT
same
fraction
needed
model,
achieving
around
30%
using
alone.
JACS Au,
Год журнала:
2025,
Номер
5(5), С. 2294 - 2308
Опубликована: Апрель 23, 2025
Deep
generative
models
yielding
transition
metal
complexes
(TMCs)
remain
scarce
despite
the
key
role
of
these
compounds
in
industrial
catalytic
processes,
anticancer
therapies,
and
energy
transition.
Compared
to
drug
discovery
within
chemical
space
organic
molecules,
TMCs
pose
further
challenges,
including
encoding
bonds
higher
complexity
need
optimize
multiple
properties.
In
this
work,
we
developed
a
model
for
inverse
design
ligands
complexes,
based
on
junction
tree
variational
autoencoder
(JT-VAE).
After
implementing
SMILES-based
metal-ligand
bonds,
was
trained
with
tmQMg-L
ligand
library,
allowing
generation
thousands
novel,
highly
diverse
monodentate
(κ1)
bidentate
(κ2)
ligands,
imines,
phosphines,
carbenes.
Further,
generated
were
labeled
two
target
properties
reflecting
stability
electron
density
associated
homoleptic
iridium
TMCs:
HOMO-LUMO
gap
(ϵ)
charge
center
(q
Ir).
This
data
used
implement
conditional
that
from
prompt,
single-
or
dual-objective
optimizing
either
both
ϵ
q
Ir
interpretation
optimization
trajectories.
The
optimizations
also
had
an
impact
other
properties,
dissociation
energies
oxidative
addition
barriers.
A
similar
implemented
condition
by
solubility
steric
bulk.
Journal of Chemical Theory and Computation,
Год журнала:
2024,
Номер
20(15), С. 6706 - 6716
Опубликована: Июль 31, 2024
One
commonly
observed
binding
motif
in
metalloproteins
involves
the
interaction
between
a
metal
ion
and
histidine's
imidazole
side
chains.
Although
previous
imidazole-M(II)
parameters
established
flexibility
reliability
of
12–6–4
Lennard-Jones
(LJ)-type
nonbonded
model
by
simply
tuning
ligating
atom's
polarizability,
they
have
not
been
applied
to
multiple-imidazole
complexes.
To
fill
this
gap,
we
systematically
simulate
complexes
(ranging
from
one
six)
for
five
ions
(Co(II),
Cu(II),
Mn(II),
Ni(II),
Zn(II))
which
appear
metalloproteins.
Using
extensive
(40
ns
per
PMF
window)
sampling
assemble
free
energy
association
profiles
(using
OPC
water
standard
HID
charge
models
AMBER)
comparing
equilibrium
distances
DFT
calculations,
new
set
was
developed
focus
on
energetic
geometric
features
The
obtained
agree
with
experimental
calculated
distances.
validate
our
model,
show
that
can
close
thermodynamic
cycle
metal-imidazole
up
six
molecules
first
solvation
shell.
Given
success
closing
cycles,
then
used
same
extended
method
other
(Ag(I),
Ca(II),
Cd(II),
Cu(I),
Fe(II),
Mg(II))
obtain
parameters.
Since
these
reproduce
one-imidazole
geometry
accurately,
hypothesize
will
reasonably
predict
higher-level
coordination
numbers.
Hence,
did
extend
analysis
Overall,
results
shed
light
metal–protein
interactions
emphasizing
importance
ligand–ligand
metal-π-stacking
within
Journal of Chemical Information and Modeling,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 25, 2024
Diffusion
models
have
emerged
as
powerful
tools
for
molecular
generation,
particularly
in
the
context
of
3D
structures.
Inspired
by
nonequilibrium
statistical
physics,
these
can
generate
structures
with
specific
properties
or
requirements
crucial
to
drug
discovery.
were
successful
at
learning
complex
probability
distributions
geometries
and
their
corresponding
chemical
physical
through
forward
reverse
diffusion
processes.
This
review
focuses
on
technical
implementation
tailored
generation.
It
compares
performance,
evaluation
methods,
details
various
used
generation
tasks.
We
cover
strategies
atom
bond
representation,
architectures
denoising
networks,
challenges
associated
generating
stable
also
explores
applications
de
novo
design
related
areas
computational
chemistry,
such
structure-based
design,
including
target-specific
docking,
dynamics
protein-ligand
complexes.
conditional
properties,
conformation
fragment-based
design.
By
summarizing
state-of-the-art
this
sheds
light
role
advancing
discovery
current
limitations.
Journal of Chemical Theory and Computation,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 9, 2024
The
design
of
transition-metal
complexes
(TMCs)
has
drawn
much
attention
over
the
years
because
their
important
applications
as
metallodrugs
and
functional
materials.
In
this
work,
we
present
an
extension
our
recently
reported
approach,
LigandDiff
[Jin
et
al.
Journal of Chemical Theory and Computation,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 17, 2025
A
key
step
in
interpreting
gas-phase
ion
mobility
coupled
with
mass
spectrometry
(IM-MS)
data
for
unknown
structure
prediction
involves
identifying
the
most
favorable
protonated
structure.
In
gas
phase,
site
of
protonation
is
determined
using
proton
affinity
(PA)
measurements.
Currently,
and
ab
initio
computation
methods
are
widely
used
to
evaluate
PA;
however,
both
resource-intensive
time-consuming.
Therefore,
there
a
critical
need
efficient
estimate
PA,
enabling
rapid
identification
complex
organic
molecules
multiple
binding
sites.
this
work,
we
developed
fast
accurate
method
PA
by
descriptors
combination
machine
learning
(ML)
models.
Using
comprehensive
set
186
descriptors,
our
model
demonstrated
strong
predictive
performance,
an
R2
0.96
MAE
2.47
kcal/mol,
comparable
experimental
uncertainty.
Furthermore,
designed
quantum
circuits
as
feature
encoders
classical
neural
network.
To
effectiveness
hybrid
quantum-classical
model,
compared
its
performance
traditional
ML
models
reduced
derived
from
full
set.
correlation
analysis
showed
that
quantum-encoded
representations
have
stronger
positive
target
values
than
original
features
do.
As
result,
outperformed
counterpart
achieved
consistent
same
on
noiseless
simulator
real
hardware,
highlighting
potential
predictions.
The
design
of
transition
metal
complexes
has
drawn
much
attention
over
the
years
because
their
important
applications
as
metallodrugs
and
functional
materials.
In
this
work,
we
present
an
extension
our
recently
reported
approach,
LigandDiff.
new
model,
which
call
multi-LigandDiff,
is
more
flexible
greatly
outperforms
its
predecessor.
This
scaffold-based
diffusion
model
allows
de
novo
ligand
either
with
existing
ligands
or
without
any
ligand.
Moreover,
it
users
to
predefine
denticity
generated
Our
results
indicate
that
multi-LigandDiff
can
generate
well-defined
great
transferability
regard
metals
coordination
geometries.
terms
application,
successfully
designs
338
Fe(II)
SCO
from
only
47
experimentally
validated
complexes.
And
these
are
configurationally
diverse
reasonable.
Overall,
show
ideal
tool
novel
scratch.
The
design
of
transition
metal
complexes
has
drawn
much
attention
over
the
years
because
their
important
applications
as
metallodrugs
and
functional
materials.
In
this
work,
we
present
an
extension
our
recently
reported
approach,
LigandDiff.
new
model,
which
call
multi-LigandDiff,
is
more
flexible
greatly
outperforms
its
predecessor.
This
scaffold-based
diffusion
model
allows
de
novo
ligand
either
with
existing
ligands
or
without
any
ligand.
Moreover,
it
users
to
predefine
denticity
generated
Our
results
indicate
that
multi-LigandDiff
can
generate
well-defined
great
transferability
regard
metals
coordination
geometries.
terms
application,
successfully
designs
338
Fe(II)
SCO
from
only
47
experimentally
validated
complexes.
And
these
are
configurationally
diverse
reasonable.
Overall,
show
ideal
tool
novel
scratch.