Predictive Multivariate Linear Regression Analysis Guides Successful Catalytic Enantioselective Minisci Reactions of Diazines

Article abstract of DOI:10.1021/jacs.9b11658

The Minisci reaction is one of the most direct and versatile methods for forging new carbon-carbon bonds onto basic heteroarenes: A broad subset of compounds ubiquitous in medicinal chemistry. While many Minisci-type reactions result in new stereocenters, control of the absolute stereochemistry has proved challenging. An asymmetric variant was recently realized using chiral phosphoric acid catalysis, although in that study the substrates were limited to quinolines and pyridines. Mechanistic uncertainties and nonobvious enantioselectivity trends made the task of extending the reaction to important new substrate classes challenging and time-intensive. Herein, we describe an approach to address this problem through rigorous analysis of the reaction landscape guided by a carefully designed reaction data set and facilitated through multivariate linear regression (MLR) analysis. These techniques permitted the development of mechanistically informative correlations providing the basis to transfer enantioselectivity outcomes to new reaction components, ultimately predicting pyrimidines to be particularly amenable to the protocol. The predictions of enantioselectivity outcomes for these valuable, pharmaceutically relevant motifs were remarkably accurate in most cases and resulted in a comprehensive exploration of scope, significantly expanding the utility and versatility of this methodology. This successful outcome is a powerful demonstration of the benefits of utilizing MLR analysis as a predictive platform for effective and efficient reaction scope exploration across substrate classes.

Full text of DOI:10.1021/jacs.9b11658

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Article
Predictive Multivariate Linear Regression Analysis Guides
Successful Catalytic Enantioselective Minisci Reactions of Diazines
Jolene P Reid, Rupert S.J. Proctor, Matthew S Sigman, and Robert J Phipps
J. Am. Chem. Soc., Just Accepted Manuscript • DOI: 10.1021/jacs.9b11658 • Publication Date (Web): 11 Nov 2019
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Predictive Multivariate Linear Regression Analysis Guides Successful
Catalytic Enantioselective Minisci Reactions of Diazines
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Jolene P. Reid,†,§ Rupert S. J. Proctor,‡,§ Matthew S. Sigman,*,† and Robert J. Phipps,*,‡
†Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States
‡Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
ABSTRACT: The Minisci reaction is one of the most direct and versatile methods for forging new carbon-carbon bonds
onto basic heteroarenes, a broad subset of compounds ubiquitous in medicinal chemistry. Whilst many Minisci-type
reactions result in new stereocenters, control of the absolute stereochemistry has proved challenging. An asymmetric
variant was recently realized using chiral phosphoric acid catalysis, although in that study the substrates were limited to
quinolines and pyridines. Mechanistic uncertainties and non-obvious enantioselectivity trends made the task of extending
the reaction to important new substrates classes challenging and time-intensive. Herein, we describe an approach to
addressing this problem through the rigorous analysis of the reaction landscape guided by a carefully designed reaction
dataset and facilitated through multivariate linear regression (MLR) analysis. These techniques permitted the development
of mechanistically informative correlations providing the basis to transfer enantioselectivity outcomes to new reaction
components, ultimately predicting pyrimidines to be particularly amenable to the protocol. The prediction of
enantioselectivity outcomes for these valuable, pharmaceutically-relevant motifs were remarkably accurate in most cases
and resulted in a comprehensive exploration of scope, significantly expanding the utility and versatility of this methodology.
This successful outcome is a powerful demonstration of the benefits of utilizing MLR analysis as a predictive platform for
effective and efficient reaction scope exploration across substrate classes.
forming process.7 We recently disclosed a strategy that
enabled influence to be exerted over both of these
1. Introduction
First developed into a general synthetic process by
Minisci and co-workers in the late 1960s, the addition of
nucleophilic radicals to electron-deficient heteroarenes
selectivity aspects for the addition of N-acyl, α-amino
radicals to a range of pyridines and quinolines.8 Jiang and
co-workers subsequently demonstrated that this strategy
has arguably become the leading method for direct carbon-
could also be applied to isoquinolines.9 Our approach was
carbon bond formation onto heteroaromatic scaffolds.1
founded on the use of a chiral phosphoric acid catalyst to
The ubiquity of pyridines, quinolines and the numerous
activate the substrate, which we anticipated was able to
derivatives thereof as structural features in molecules of
subsequently engage in a network of non-covalent
biological interest has rendered so-called ‘Minisci-type’
interactions (NCI’s) with the radical cation intermediate in
chemistry an indispensable tool for medicinal chemists.2
the transition state (TS) for selectivity-determining
Whilst the original conditions developed by Minisci for
deprotonation
(Figure
1A).10
However,
the
radical generation are still widely applied, the past decade
in particular has seen tremendous attention paid to the
development of new protocols for Minisci-type reactions.3
The major emphasis of these advances has been on
enhanced approaches for radical generation. Indeed,
Minisci-type chemistry has, to some degree, become a
testbed for the latest developments in emerging areas such
as photoredox catalysis4 and electrochemistry.5 However,
the Minisci reaction presents several fascinating selectivity
challenges to the synthetic chemist. The first is
regioselectivity, since on heteroarenes the LUMO
coefficients can be very similar at multiple positions.6 The
second is the question of whether a prochiral nucleophilic
radical may be coaxed into forming a new stereocenter in
an enantiocontrolled manner during the C‒C bond
enantioselectivity trends with respect to both catalyst and
substrate were not obvious. Even modest structural
modifications resulted in substantial differences (Figure
1B), making immediate extension of the protocol to other
substrate types challenging. The notion that selectivity
could be influenced to such an extent by minor structural
modifications to the substrate is intriguing as it alludes to
subtle albeit important molecular features impacting
asymmetric catalysis. In targeting the understanding and
prediction of substrate efficacy, we approached this
problem within the context of a modern physical organic
analysis. In this scenario enantioselectivity11 or site-
selectivity12 values can report on specific interactions
between catalyst and substrate. Specifically, we reasoned
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that by designing a data set in which the structural features
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of the catalyst and
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A. Mechanistic considerations
B. Reaction sensitivities
quinoline products with TRIP
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CO2 + PhthN
O
N
R
N
O
O
H
R
N
N
Bn
NHAc
N
O
N
H
NHAc
1a
97% ee
1b
86% ee
1c
73% ee
IrIII
NHAc
NHAc
NHAc
O
R
Ac
N
Photoredox
cycle
O
OH
h
IrII
O
pyridine products with TCYP
P
*
Chiral
phosphoric
acid cycle
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P
O
O
CO2Me
O
O
*IrIII
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MeO2C
NC
*
N
2a
39% ee
N
N
2c
61% ee
H+
R
2b
90% ee
NHAc
NHAc
NHAc
iPr
R
R
N
H
N
N
Ac
N
NHAc
NHAc
H
chiral phosphoric acids with model product 1a
H
H
O
O
How do the catalyst and substrate
interact at the transition state?
Can this be probed using catalyst
and substrate sensitivities?
If successful can these observations
be translated to unique substrates?
P
*
Cy
Cy
iPr
O
O
enantiodetermining
deprotonation
F3C
CF3
Cy
iPr
47% ee
49% ee
95% ee
97% ee
C. Modelling goals
Ar
N
O
O
O
P
O
OH
chemical
observation
transfer
N
Goal: transfer chemical observations
from known quinoline and pyridine
data sets to unique substrate types
such as pyrimidines and pyrazines
N
O
O
N
N
NHAc
O
Ar
NHAc
N
N
Ac NH
conditions
N
N
NHAc
NHAc
Training data for statistical model development
Figure 1. The application of statistical analysis tools to reaction development. (A) Working mechanistic hypothesis for asymmetric
radical addition to heteroarenes. (B) Substrate and catalyst sensitivities deployed as a mechanistic probe. (C) The mechanistic
principles leading to enantioselective catalysis captured by the statistical models can be transferred to genuinely different
structural motifs not contained in the training dataset, facilitating reaction development.
substrate were appropriately modified, effective
correlations could reveal the underlying causal
interactions. It was anticipated that such an analysis would
not only provide key insights into reaction mechanism, but
also the ability to predict performance of new substrate
types to ultimately expand the scope of the process. With
regard to the latter, our initial report had only explored the
reaction of pyridines and quinolines. Yet, the prevalence of
diverse heterocycles possessing additional heteroatoms in
medicinal compounds led us to question the broader
applicability of this enantioselective Minisci method.2b If
the selectivity discriminants were consistent for a range of
substrates, it may be possible to quantitatively transfer the
insights gained from the correlations to the prediction of
unique substrates not included in the training sets (Figure
1C). Moreover, it is widely acknowledged by medicinal
chemists that increasing the three-dimensionality of
scaffolds in lead molecules enhances the odds of success as
a drug candidate.13 Three-dimensionality inevitably leads
to stereoisomers, which often elicit distinct biological
activity. As such, a method to predict the viability of
directly appending chiral scaffolds to a range of basic
heteroarenes with control of absolute stereochemistry
would likely have significant impact in pharmaceutical
research. To this end, we report a study employing
predictive, statistical modelling techniques to relate both
catalyst and substrate structures to selectivity outcomes.
With statistical models that describe the general
mechanistic features of the system, we can quantitatively
transfer chemical insights to new substrate components.
Furthermore, our model has identified pyrimidines and
pyrazines to be amenable to the reaction conditions,
successfully predicting protocol extension to the use of
these valuable basic heteroarene motifs.
2. Results and Discusssion
Data Set Design and Modeling. Intrigued by the
observed lack of selectivity for certain substrate subsets in
the initial exploration of reaction space, and driven by the
importance of accessing varied chiral heteroarene building
blocks, we initiated a study into the scope and limitations
of the enantioselective Minisci protocol. Despite
previously reporting
a
collection of experimental
observations for this chemistry, we anticipated that a
designed data set in which both the substrate and catalyst
were systematically modified would allow effective
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correlation and prediction of substrate performance. In selectivity, to be dramatically reduced. For example,
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approaching the design of such a data set, we sought to first
establish the enantioselectivity range accessible by
changing both the reactants and catalysts. This step was
used to facilitate rapid identification of the features that
most perturbed the enantioselectivity of the process to
inform the correct choice of combinations for a matrix. In
regard to structural changes, with the generally optimal
catalyst for each substrate subset, pyridines were the most
sensitive (39-93% ee, with TCYP) and quinolines the least
(73-97% ee, with TRIP). Perhaps most notably, small steric
aromatic derived catalysts all contain a 6-membered ring
with different substituents at these positions, by contrast,
non-aromatic derived catalysts do not. Therefore, we only
consider BINOL-derived phosphoric acids with aryl
substituents at the 3 and 3’ positions, the most commonly
used class for asymmetric catalysis. As such, caution
should be taken in extrapolating outcomes to other classes,
which can prove superior in some situations (see SI).15
9
With the appropriate libraries of the substrates and
catalysts in hand, the enantioselective outcome of each
combination was measured as depicted in Figure 2. From
this visual analysis, catalysts with proximal steric bulk
(Figure 2, left hand side) demonstrate a unique response as
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profiles
on
the
N-heterocycles
reduced
enantioselectivities, however, electronics effects were
much subtler. To probe the effect of the 3 and 3’
substituents on the catalyst, we examined a variety of
a
function of substrate compared to other CPAs.
BINOL-derived
phosphoric
acids.
The
screen
Specifically, a strong dependence of the ee on the
heterocycle substituent(s) was observed, resulting in a
G‡ range of ~1.7 kcal/mol. In contrast, the reaction was
less sensitive to these substitution patterns with the
remaining catalysts and the enantioselectivities remained
relatively poor. The unique behavior of the 2,6-substituted
catalysts is consistent with enhanced stereocontrolling
interactions with this catalyst subset.
demonstrated that reasonably large groups at the 3,3’
positions were necessary for high ee’s, a finding common
in similar transformations.14 In targeting the description of
general trends, seven substrates (A-G) were selected that
evenly covered the range of enantioselectivities
representative of the different substitution patterns
presumed to influence the selectivity (Figure 2).
Simultaneously, eight phosphoric acid catalysts were
prepared with variable substitution at the 3 and 3’ positions
of the BINOL-backbone. TRIP, 2,6-methyl, and 2-iPr
catalysts were selected to probe proximal sterics, while 3,5-
methyl and 3,5-tBu catalysts were chosen for
understanding remote steric effects. Two other catalysts, 1-
naphthyl and 9-phenanthryl, were prepared to evaluate the
possibility of attractive non-covalent interactions, as
opposed to repulsive steric ones. Finally, phenyl was
intended to serve as a deconstructed derivative of each
scenario outlined above to probe any isolated effects. This
training set was used not only to provide requisite
structural changes as a function of enantioselectivity but
also incorporates sufficient overlapping molecular feature
space required for the development of comprehensive
parameter libraries and statistical analysis. For example,
TIPSY, which has large SiPh3 groups at the 3 and 3’
positions is reasonably effective (product A, 75% ee) but its
inclusion in the training set would render the parameter
space, that is required to connect changes in structure to
To truly interrogate the interactions between catalyst
and substrate, we sought to employ multivariate linear
regression analysis (MLR).16 In this approach, parameter
sets describing the important structural features of the
reaction components are related to selectivity outputs
expressed as G‡. The resulting mathematical equation
generally consisting of multiple terms, can be deployed to
predict the outcome when features are adjusted.
Traditionally, parameter selection is accomplished using
candidate structures, which can either be the entire
molecule or
a simplified structural surrogate (for
substrates these are often starting materials to mirror
Hammett type analysis). In this case, we used the product
structures as it combines both reactants while also
expanding the features one may extract for aiding
correlation development. We viewed this as a simple yet
crucial means of describing the molecular features most
relevant to the enantiodetermining step. To build the
parameter set, computation optimizations were performed
on these structures at the M06-2X/def2-TZVP level of
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quinoline and pyridine products
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3
4
iPr
NHAc
Ph
iPr
NHAc
N
N
N
NHAc
Ph
A
B
C
NC
MeO2C
NC
5
6
7
iPr
NHAc
iPr
NHAc
iPr
iPr
NHAc
N
N
N
N
NHAc
D
E
F
G
chiral phosphoric acids
8
9
iPr
iPr
iPr
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9-Phen
tBu
2-iPr
2,6-Me
iPr
TRIP
tBu
Ph
3,5-Me
1-Nap
3,5-tBu
Figure 2. Graphical representation of substrate structure-selectivity trends as a function of catalysts. Colors partition
catalysts that have 2 or 2,6-substituents which exhibit a unique response as a function of substrate, compared to other
CPAs.
theory wherein Natural Bond Orbital (NBO) charges, IR
vibrations and Sterimol values were collected to probe
structural effects.
Through an iterative MLR modeling process (see SI for
workflow), the combination of steric and electronic
parameters resulted in the model depicted in Figure 3.
Both Leave-one-out (LOO) and external validation, in
which the dataset is partitioned pseudo-randomly into
70:30 training:validation sets suggests a relatively robust
model. The consistency in descriptors of the top 10 models
as determined by their statistical scores and predictability,
demonstrates that interpretability of a singular model does
not affect the overall analysis(see SI for full details).
Interestingly, the largest coefficients in the depicted
normalized model correspond to the product, with the
heterocycle and RAE represented by seemingly individual
components. Variations in N-heterocycle component of
the product can be described by NBONHet, B1NHet and
NBORAE. In considering NBORAE, this term acts as a
Selectivity is dependent on catalyst and substrate steric profiles
NBOC2
NC
NBORAE
descriptor for both heterocycle and RAE structural
features. This illustrates the advantage of simplifying
correlation equations to collective terms through
deploying product structures that combine both reactants
as the parameter acquisition platform. However, it is likely
that the descriptor is reading out more than one physical
effect in the diastereomeric TS structures making precise
interpretations difficult. Ultimately, this analysis implies
that the substrate effect on enantioselectivity is mostly
additive but suggests there could be some circumstances
where correct matching of heterocycle and RAE may be
beneficial.
NBONhet
LRAE
iPr
H
N
O
O
O
P
HN
B1Nhet
O
iPOas
O
Figure 3. MLR correlation reveals enantioselectivity is
dependent on catalyst and substrate steric profiles as
represented by various catalyst/product terms.
Consistent with other studies, the overall incorporated
terms support steric bulk as the major catalyst selectivity
discriminant.17 Specifically, both reasonably large 3,3’
substituents and N-heterocycles were important for high
levels of enantioselectivity. This is congruent with the
hypothesis that TSminor is disfavored as a consequence of
energetically penalizing steric repulsions with the catalyst
substituents enhanced through large substrate sterics.
The inclusion of LRAE with a negative coefficient suggests
that TSmajor is also sensitive to the substrate molecular
features. In other words, longer substituents introduce
enhanced steric effects with the catalyst in the TS leading
to the observed product, ultimately favoring formation of
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the opposite enantiomer. Since the Sterimol L term is a been included in our initial report, but to do so in a rational
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conformationally sensitive parameter, it may also describe
the role of a preferred geometry.18 Indeed, surveying the
enantioselectivities of the reactions forming B and C, in
which they differ only by RAE, shows that B performs
better overall despite -iPr appearing to be shorter than -
CH2Bn. However, computation optimizations demonstrate
that B can adopt more compact arrangements and smaller
L values at the RAE than C, clarifying this non-intuitive
trend. This highlights that substrate dynamics are also
important in determining selectivity.
manner which would not involve ‘screening’ numerous
substrates in search of hits with high ee. We envisaged that
successful application of MLR analysis to reaction scope
expansion would be a very effective showcase of the
practical benefits of this approach. However, before
progressing to new heterocycle classes, we first sought to
evaluate the model’s prediction performance on the
previously reported dataset to consider the feasibility of
this endeavor.8 As a first assessment, we evaluated the
ability to predict five additional reactions, involving
catalysts with various 3,3’ substituents, with a model
substrate contained in our training set (Figure 4A). The
ability to predict in this reaction dimension would be
particularly useful if the optimal catalyst for a specific
substrate combination was not contained in the training
set. Treating these as virtual predictions this set was
predicted accurately, with an average absolute G‡ error
of 0.29 kcal/mol.
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The impact of catalyst and substrate on regioselectivity
(2- vs. 4-position) was also probed. Since the inherent
regioselectivity of the mechanism is masked by the
pyridine subset in which 4-addition does not occur with
3,3’-substituted acids, only the quinoline substrates (A - C),
exhibiting variable regioselectivity as a function of catalyst,
were further investigated. Employing the same modelling
techniques led only to complex models. This observation is
compounded by the training set restriction in terms of data
range and structure. A correlation of the C2:C4 isomeric
ratio (rr) with the enantioselectivity of the product reveals
a linear relationship, in which, as the ee increases the rr
generally increases (see SI). This suggests that for the
quinoline substrate subset, the undesired 4-regioisomer
could arise from an unselective pathway. This becomes
evident when the enantioselectivity of the 4-isomer
product was measured, where possible, resulting in low
enantioselectivity values (<15% ee).
As a second case study, the model was assessed in the
same manner with twenty-five additional reactions
involving various substrate subsets catalyzed by TRIP or
TCYP.
Examples were selected based on range in
enantioselectivity (61-97% ee) and substrate structure (full
list can be found in the SI). This is a more challenging
scenario as some substrate and catalyst components are
not explicitly included in the training set. Again, accurate
prediction of the outcomes was construed using the model,
with an average absolute error of 0.31 kcal/mol and 18
examples predicted within 5% ee (Figure 4B). These results
suggest that the ability to effectively extrapolate to new
reaction components results from a set of general
transition state features that are fundamentally similar
across the reaction range.
Reaction Design. Whilst the obtained model, shown in
Figure 3, provides insightful mechanistic information on
the transformation, the clear practical utility lies in its
ability to predict the performance of unique substrate
classes, thereby directing future synthetic efforts. If
effective out-of-sample prediction were possible, the
model could estimate the impact of a new heterocycle,
RAE, and/or catalyst on selectivity, provided that the
prediction platforms incorporated sufficient overlap with
the training set. Typically, exploration of the synthetic
scope of a new enantioselective chemical reaction involves
evaluation of a large number of substrates, only a
proportion of which yield the desired high levels of
enantiomeric excess. This can be a time and resource-
consuming process, particularly when substrates require
multi-step synthesis. Conversely, in target-driven synthesis
only a single specific substrate is of interest and a number
of different synthetic approaches may be considered. A
reliable, predictive mathematical model, accessible to
bench chemists, has the potential to narrow down the
myriad options in the latter scenario and greatly accelerate
reaction scope exploration in the former. The workflow for
ee prediction is straightforward and is initiated by locating
the ground state of the targeted reaction variable by DFT
computation, collecting the requisite parameters and
submitting them to the equation (see SI for tutorial).
On the basis of the key parameters in the model, we
envisaged that pyrimidines should, in principle, constitute
excellent substrates as the inclusion of the second ring
nitrogen would be expected to increase the magnitude of
the NBONhet term significantly. Pyrimidines are ubiquitous
in pharmaceuticals, agrochemicals, and small molecules of
medicinal interest and so demonstration of the protocol on
this class would be of substantial practical value. Thus, we
evaluated
a number of reactions involving various
electronically and sterically unique pyrimidine and
pyrazine substrates, guided by our predictive model. A
phenylalanine-derived RAE was selected as the radical
precursor, along with either TRIP or TCYP as catalyst. The
predictions obtained from the model are shown in Scheme
1 alongside the experimental results that were ultimately
obtained, and pleasingly the agreement was generally
excellent. Furthermore, the observed enantioselectivities
were typically superior to the use of pyridines, a previously
explored subset. Each measured enantioselectivity in
Scheme 1 was predicted with an average absolute G‡
error of 0.39 kcal/mol (13 examples within 5 % ee),
demonstrating the ability of the model to extrapolate
effectively to an entirely new class of substrates.
In the context of the enantioselective Minisci reaction,
we sought to expand the scope of the heterocyclic
component beyond the pyridines and quinolines that had
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1
2
A. Variation in catalyst
B. Variation in substrate(s) and/or catalyst
Ar
3
4
5
6
O
O
O
OH
P
O
O
N
O
O
N
O
O
N
N
O
O
Ar
NHAc
NHAc
TRIP or TCYP
iPr
iPr
optimized conditions
optimized conditions
Ac NH
N
N
Ac NH
7
NHAc
N
N
8
9
average prediction error (5 examples) 47-96% ee observed
61-97% ee observed
82-98 % ee predicted
18 examples within 5%
average prediction error (25 examples)
0.31 kcal/mol
0.29 kcal/mol
63-97 % ee predicted
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select examples
select examples
OAc
O
Cy
Cy
Ph
Ph
iPr
iPr
MeO
iPr
N
N
Ph
NHAc
N
Cy
Ph
NHAc
NHAc
observed 84% ee
model = 79% ee
(error = -0.17 kcal/mol)
observed 95% ee
model = 97% ee
(error = 0.43 kcal/mol)
observed 96% ee
model = 94% ee
(error = -0.23 kcal/mol)
observed 97% ee
model = 97% ee
(error = 0.01 kcal/mol)
observed 92% ee
model = 95% ee
(error = 0.26 kcal/mol)
observed 90% ee
model = 85% ee
(error = -0.26 kcal/mol)
Figure 4. Prediction platforms. (A) Assessing prediction capabilities with various 3,3’ substituted phosphoric acids. (B)
Prediction of assorted reaction systems containing substrate and catalyst components not explicitly included in the training
set.
Specifically, unsubstituted pyrimidine reacted with
complete regioselectivity at the C4 position to deliver
product 1 in 88% ee when using TCYP as catalyst (TRIP
gave 78% ee, with the model predicting 83% ee). Given the
lack of steric features on unadorned pyrimidine, we
regarded this as a highly encouraging result, the moderate
yield being due to incomplete conversion rather than
deleterious pathways. Whilst superficially surprising that a
heteroarene with no steric features should perform well,
close examination of key parameters in the model reveals
that the more positive NBO values associated with
pyrimidine, a result of inclusion of the second ring
nitrogen, largely compensate for a lower B1NHet term.
Furthermore, the ability of the model to accurately reflect
the outcomes with different phosphoric acids highlights its
utility for predicting the right catalyst for a particular
substrate, obviating the need for extensive catalyst
screening for each substrate. A bromide substituent was
tolerated at the C5 position with only a slight decrease in
ee (2, 83% ee) and a methyl was similarly incorporated at
C4 (3, 85% ee). When the pyrimidine possessed a
reaction made it possible to stop at mono alkylation (4,
94% ee) or progress all the way to dialkylation (5, 20:1 dr,
>99% ee (major diastereomer)). A variety of more complex
2-methylpyrimidine substrates were well-tolerated,
including 4-Me (6, 91% ee), 4-Cl (7, 97% ee), 4-Ph (8, 97%
ee), 5-Ph (9, 99% ee) and 5-Br (10, 97% ee). The absolute
stereochemistry of the products are predicted to be
consistent with the original systems as confirmed by the X-
ray crystallographic analysis after recrystallization of 10.
The stereochemistry of the remainder of the entries are
assigned by analogy. Aryl substitution was accommodated
at the C2 position (11, 94% ee) and the model predicted this
significant structural perturbation with remarkable
accuracy. When progressing to the 2-methoxypyrimidines
12 and 13, we obtained some of the highest
enantioselectivities
observed
thus
far
in
any
enantioselective Minisci reaction (>99% ee for the bromo-
functionalized 12 and 99% ee for chloro-functionalized 13),
a result of matching multiple positive structural effects. For
these two substrates, moderate conversions led us to raise
the catalyst loading to 10 mol% with longer reaction times
of 48h to obtain the yields shown. We sought to test the
predictive power of the model on pyrimidine in
combination with RAEs other than the phenylalanine-
derived variant used thus far. Therefore, RAEs derived
from valine, homophenylalanine and leucine were
evaluated. The experimental results were in excellent
substituent
at
C2,
enantioselectivity
increased
significantly. Once again, the model clearly explains why
such substrates should be particularly amenable, since
both the NBO and B1 terms are now large and positive. For
substrates such as 4, possessing a substituent only at C2,
careful control of the stoichiometry and time of the
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agreement with the predicted values (Scheme 1, 14-16).
Homophenylalanine- (14) and leucine-derived (15) RAEs
1
2
gave significantly lower ee than phenylalanine, which are
consistent with observations utilizing quinolines and
pyridines (Fig 1B). For the valine-derived RAE (16), the
model accurately predicted moderate enantioselectivity
(69% ee), contrasting the superior results this RAE had
given with quinolines and pyridines. By analyzing terms in
the statistical model, the lower enantioselectivity can be
attributed to the more negative NBORAE, which overrides
any beneficial impact garnered from the more negative
LRAE and positive NBONhet terms. Furthermore, this
outlines an instance in which correct matching of
heterocycle and RAE is beneficial. We also explored
several examples of pyrazine substrates and pleasingly
observed that various combinations of methyl substitution
worked effectively and were accurately predicted (17, 90%
ee and 18, 91% ee). One limitation to acknowledge is that
the model can only guide users of the methodology on
assessing selectivity outputs and therefore will not be
capable of predicting reactivities. This is exemplified by the
fact that no reactivity was observed with pyridazines and
quinoxalines under our conditions (see SI). Quinazoline
reacts poorly giving product 19 in 56% ee. Predicted at 75%
ee, this is within the average error of the model (G‡ error
of 0.41 kcal/mol compared with averaged diazine
prediction set of 0.39 kcal/mol).
3
4
5
6
7
8
9
Scheme 1. Substrate scope of enantioselective Minisci
reaction on pyrimidines and pyrazines.
10
11
12
13
14
15
16
17
18
19
20
21
22
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58
59
60
Whilst the observed enantioselectivities for the
substrates presented in Scheme 1 generally show good
agreement with the prediction for both high and low ee
examples, we discovered that substrates incorporating an
amino- substituent between the two nitrogen atoms
(Scheme 2, 20 and 21) gave results that were rather lower
than predicted. We speculate that additional hydrogen
bonds formed with these groups are likely interrupting the
hydrogen bonding network leading to stereoinduction, a
critical catalyst-substrate interaction expressed by the
model terms. Ultimately, these are unique contacts that
are not represented in the training set and demonstrate a
limitation of the present model.
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Page 8 of 11
membered vs. 6-membered ring). To address this
1
featurization challenge, we used 0 digits as descriptors for
the missing benzothiazole components. By deploying the
adapted descriptor set and the training model, the
resultant extrapolation of substrate space predicted only
modest enantioselectivities, an observation validated by
experiment (Scheme 2, 22). This result is compelling in that
we could reach an informed decision about pursuing
benzothiazoles as a substrate class. However, the predicted
enantioselectivities for 22 were higher than observed but
the G‡ error of 0.41 kcal/mol is comparable to the
averaged diazine prediction set (0.39 kcal/mol) suggesting
the source of the error may be systematic. Taken together,
these examples showcase that the model’s predictive
capabilities are not limited to classifying published
datasets, but can be applied to analyze and predict new
reactions even in situations where multiple components
are varied. Particular highlights of this protocol are the
uniformity of the conditions employed for the diverse set
of heteroarenes and the ability to extrapolate to new
substrate types in the absence of persuasive mechanistic
information.
[Ir(dF(CF3)ppy)2(dtbpy)]PF6
O
2
3
4
5
6
7
8
9
(R)-TRIP or (R)-TCYP
het
het
PhthNO
Ph
NHAc
Ph
NHAc
dioxane
blue LEDs, 14 or 48 h
Pyrimidines:
Br
N
N
N
N
Ph
N
Ph
NHAc
N
Ph
NHAc
NHAc
2b
3b
1b
62% yield
83% ee
(predicted 84% ee)
55% yield
85% ee
(predicted 90% ee)
44% yield
88% ee
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
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40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
(predicted 89% ee)
AcHN
Ph
N
N
N
N
N
Ph
NHAc
Ph
NHAc
N
Ph
NHAc
5c
6a
4a
86% yield
20:1 dr
>99% ee
36% yield
91% ee
(predicted 96% ee)
45% yield
94% ee
(predicted 95% ee)
Cl
N
Ph
N
Ph
N
N
Ph
NHAc
N
Ph
NHAc
N
Ph
NHAc
7a
8a
9a
63% yield
97% ee
(predicted 96% ee)
93% yield
97% ee
(predicted 96% ee)
54% yield
99% ee
(predicted 89% ee)
Scheme 2. Substrates revealed as limitations.
Ph
S
N
N
N
Br
N
BocHN
N
Ph
NHAc
NHAc
H2N
N
Ph
NHAc
N
Ph
NHAc
21
20
22
54% yield
55% ee
(predicted 97% ee)
35% yield
81% ee
(predicted 95% ee)
10a
19% yield
27% ee
(predicted 56% ee)
65% yield
97% ee
(predicted 92% ee)
(error = 0.41 kcal/mol)
Br
N
Cl
N
N
Conclusion. We have described the development of a
predictive, mathematical model for the enantioselective
Minisci addition of N-acyl, α-amino radicals to pyridines
and quinolines through careful evaluation of
catalyst/substrate training sets and parameter acquisition
platforms. The model describes the general transition state
features important for the reaction class, which ultimately
provided the basis for the transfer of experimental
observations from one substrate subset to another. The
model parameters suggested that pyrimidines, with
typically larger NBO values than pyridines, should be
particularly amenable to the same reaction conditions. The
specific predictions produced by the model prompted us to
explore a range of substituted pyrimidines, as well as
several pyrazines. The accurate predictive power avoided
the need to assess a large number of substrates in order to
discover those most compatible with the method – we were
guided there directly, saving valuable time and resources.
This should provide confidence to synthetic chemists
looking to extrapolate this methodology further, to other
diverse heterocyclic classes. More broadly, this successful
outcome is a powerful demonstration of the benefits of
utilizing MLR analysis as a predictive platform for effective
and efficient reaction scope exploration in asymmetric
catalysis.
MeO
N
Ph
Ph
N
Ph
NHAc
MeO
N
Ph
NHAc
NHAc
12c
11a
33%
13c
33% yield
99% ee
44% yield
>99% ee
(predicted 97% ee)
94% ee
(predicted 95% ee)
(predicted 97% ee)
Redox Active Esters:
N
N
N
N
iPr
NHAc
Ph
N
N
iPr
NHAc
NHAc
16b
41% yield
69% ee
14
15
48% yield
65% ee
(predicted 64% ee)
23% yield
28% ee
(predicted 8% ee)
(predicted 76% ee)
Pyrazines:
Quinazoline:
N
N
N
N
N
N
Ph
NHAc
Ph
Ph
NHAc
AcHN
19a
10% yield
56% ee
17a
18a
40% yield
91% ee
61% yield
90% ee
(predicted 93% ee)
(predicted 87% ee)
(predicted 75% ee)
a 5 mol% TRIP, 14h. b 5 mol% TCYP, 14h. c 10 mol% TRIP, 48h
To test this approach on more structurally disparate
bicyclic heteroarenes, the reaction of benzothiazole was
probed. The reduction in overlapping features with our
training set structures creates a challenge in extending our
comprehensible parameter sets to this substrate class (5-
ASSOCIATED CONTENT
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Enantioselective Catalysis of Photochemical Reactions. Angew.
Experimental details, procedures, compound characterization
data, computational details, copies of 1H and 13C NMR spectra
of new compounds. This material is available free of charge via
the Internet at http://pubs.acs.org.
Chem. Int. Ed. 2015, 54, 3872-3890; (c) Silvi, M.; Melchiorre, P.,
Enhancing the potential of enantioselective organocatalysis with
light. Nature 2018, 554, 41-49.
8. Proctor, R. S. J.; Davis, H. J.; Phipps, R. J., Catalytic
enantioselective Minisci-type addition to heteroarenes. Science
2018, 360, 419-422.
9. Liu, X.; Liu, Y.; Chai, G.; Qiao, B.; Zhao, X.; Jiang, Z.,
Organocatalytic Enantioselective Addition of α-Aminoalkyl
Radicals to Isoquinolines. Org. Lett. 2018, 20, 6298-6301.
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4
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7
AUTHOR INFORMATION
Corresponding Authors
* E-mail matt.sigman@utah.edu
* E-mail rjp71@cam.ac.uk
8
9
10. For selected examples of application of non-covalent
catalysis in enantioselective radical chemistry, see: (a) Bauer, A.;
Westkamper, F.; Grimme, S.; Bach, T., Catalytic enantioselective
reactions driven by photoinduced electron transfer. Nature 2005,
436, 1139-1140; (b) Müller, C.; Bauer, A.; Maturi, M. M.; Cuquerella,
M. C.; Miranda, M. A.; Bach, T., Enantioselective Intramolecular
[2 + 2]-Photocycloaddition Reactions of 4-Substituted Quinolones
Catalyzed by a Chiral Sensitizer with a Hydrogen-Bonding Motif.
J. Am. Chem. Soc. 2011, 133, 16689-16697; (c) Rono, L. J.; Yayla, H.
G.; Wang, D. Y.; Armstrong, M. F.; Knowles, R. R.,
Enantioselective Photoredox Catalysis Enabled by Proton-
Coupled Electron Transfer: Development of an Asymmetric Aza-
Pinacol Cyclization. J. Am. Chem. Soc. 2013, 135, 17735-17738; (d)
Alonso, R.; Bach, T., A Chiral Thioxanthone as an Organocatalyst
for Enantioselective [2+2] Photocycloaddition Reactions Induced
by Visible Light. Angew. Chem. Int. Ed. 2014, 53, 4368-4371; (e)
Uraguchi, D.; Kinoshita, N.; Kizu, T.; Ooi, T., Synergistic Catalysis
of Ionic Brønsted Acid and Photosensitizer for a Redox Neutral
Asymmetric α-Coupling of N-Arylaminomethanes with
Aldimines. J. Am. Chem. Soc. 2015, 137, 13768-13771; (f) Tröster, A.;
Alonso, R.; Bauer, A.; Bach, T., Enantioselective Intermolecular [2
+ 2] Photocycloaddition Reactions of 2(1H)-Quinolones Induced
by Visible Light Irradiation. J. Am. Chem. Soc. 2016, 138, 7808-7811;
(g) Lin, J.-S.; Dong, X.-Y.; Li, T.-T.; Jiang, N.-C.; Tan, B.; Liu, X.-
Y., A Dual-Catalytic Strategy To Direct Asymmetric Radical
Aminotrifluoromethylation of Alkenes. J. Am. Chem. Soc. 2016,
138, 9357-9360; (h) Lin, L.; Bai, X.; Ye, X.; Zhao, X.; Tan, C.-H.;
Jiang, Z., Organocatalytic Enantioselective Protonation for
Photoreduction of Activated Ketones and Ketimines Induced by
Visible Light. Angew. Chem. Int. Ed. 2017, 56, 13842-13846; (i) Liu,
Y.; Liu, X.; Li, J.; Zhao, X.; Qiao, B.; Jiang, Z., Catalytic
enantioselective radical coupling of activated ketones with N-aryl
glycines. Chem. Sci. 2018, 9, 8094-8098; (j) Gentry, E. C.; Rono, L.
J.; Hale, M. E.; Matsuura, R.; Knowles, R. R., Enantioselective
Synthesis of Pyrroloindolines via Noncovalent Stabilization of
Indole Radical Cations and Applications to the Synthesis of
Alkaloid Natural Products. J. Am. Chem. Soc. 2018, 140, 3394-3402;
(k) Cao, K.; Tan, S. M.; Lee, R.; Yang, S.; Jia, H.; Zhao, X.; Qiao, B.;
Jiang, Z., Catalytic Enantioselective Addition of Prochiral Radicals
to Vinylpyridines. J. Am. Chem. Soc. 2019, 141, 5437-5443; (l)
Zheng, J.; Swords, W. B.; Jung, H.; Skubi, K. L.; Kidd, J. B.; Meyer,
G. J.; Baik, M.-H.; Yoon, T. P., Enantioselective Intermolecular
Excited-State Photoreactions Using a Chiral Ir Triplet Sensitizer:
Separating Association from Energy Transfer in Asymmetric
Photocatalysis. J. Am. Chem. Soc. 2019, 141, 13625-13634.
Author Contributions
§ J.P.R. and R.S.J.P. contributed equally.
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ACKNOWLEDGMENT
R.S.J.P. is grateful to GlaxoSmithKline and the EPSRC for a
CASE PhD studentship. R.J.P. is grateful to the Royal Society
for a University Research Fellowship and the ERC for funding
(StG 757381). J.P.R. thanks the EU Horizon 2020 Marie
Skłodowska-Curie Fellowship (grant no. 792144) and M.S.S
thanks the NIH (1 R01 GM121383) for support of this work.
Computational resources were provided from the Center for
High Performance Computing (CHPC) at the University of
Utah and the Extreme Science and Engineering Discovery
Environment (XSEDE), which is supported by the NSF (ACI-
1548562) and provided through allocation TG-CHE180003. We
are We are grateful to Dr. Andrew Bond (University of
Cambridge) for solving and refining the X-ray crystal
structure, to Dr. Holly J. Davis for initial experiments and
Jonathan Taylor (GSK) for useful discussion.
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Field Guide to Asymmetric BINOL-Phosphate Derived Brønsted
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Previously Reported
Enantioselective Minisci
Reaction
Pyrimidine + Pyrazine =
Prediction Set
Quinoline + Pyridine = Training Set
4
5
6
N
N
R
R
N
N
N
R
Chiral
Br
Phosphoric
Acid Catalyst
eg.
N
NHAc
7
8
MeO
N
Ph
NHAc
>99% ee
R
N
9
NHAc
previously demonstrated on
quinolines and pyridines
Carefully designed enantioselectivity
dataset for effective correlations
Tanslates to enable accurate
prediction and rapid scope expansion
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