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A Data Intensive Approach to Mechanistic Elucidation Applied to Chiral Anion Catalysis
Andrew J. Neel†,*, Anat Milo‡,*, Matthew S. Sigman‡, F. Dean Toste† †Department of Chemistry, University of California, Berkeley, CA 94720 ‡Department of Chemistry, University of Utah, Salt Lake City, UT 84112
Summary: An understanding of the catalyst-substrate interactions that underlie selectivity (e.g. enantio-, diasterio-, or regioselectivity) in a
given reaction is essential for the rational design of superior catalytic systems. Frequently however, it is difficult to discern how molecular features work in concert to achieve a desired outcome. We reasoned that in such instances, the outcome of every catalyst-substrate combination contains valuable information regarding their transition state interaction. Herein we describe a methodology for extracting this information in a meaningful way. This technique involves correlating various molecular descriptors with experimental enantioselectivity outcomes using linear regression techniques, and subsequently using the resulting predictive models to develop a mechanistic proposal that can be evaluated experimentally. Two case studies are presented from the field of chiral anion catalysis in which catalyst-substrate association is particularly challenging to discern. In each case, this data-intensive approach has yielded non-intuitive insights that have led to a deeper mechanistic understanding, enabling in one instance the rational design of a catalyst that displays the highest enantioselectivity observed for its system.
Asymmetric Fluorination (cont’d)
2. Asymmetric Fluorination
Acknowledgements
1. Asymmetric CDC
Design/prepare library Collect descriptors
Obtain experimental data
Develop hypothesis Experimentally validate
NNN
R
R
Big Small
ElectronRich
ElectronPoor
N NN
GeometryIR Vibrations
N
N
O
R
R
ee1 = XX
een = YY
P
O
O
OO
N R
??
OO
NNN
N NN
P OOH
tailored catalysts
B1:
σ :
HammettCharge
1. 2.
3.
5. 6.
Identify correlations
-0.5 0 0.5 1 1.5 2
-0.5
0
0.5
1
1.5
2
Measured DDG‡ HkcalêmolL
PredictedDDG‡Hkca
lêmolL
4.
Background
-0.5 0 0.5 1 1.5 2
-0.5
0
0.5
1
1.5
2
Measured DDG‡ HkcalêmolL
PredictedDDG‡Hkca
lêmolL 3
6
4
5
8
72
1
y = 0.92x + 0.07R2 = 0.91
9
10
11
N NN Br
iPr
iPr
N NNsingle iPr group= torsion w/o clash
displays torsion angleclose to 90 o
Data-Intensive Approach: Outline
Ø Ee determined for every catalyst-substrate combination (132 total)
Ø Ee of each substrate with all catalysts modeled using only catalyst descriptors4 and vice versa (23 small models total)
Ø Enantioselectivity trends not intuitive, even small modification to substrates or catalysts drastically affects enantioselectivity
Ø Hypothesis: Every data point is meaningful: even low selectivity values contain valuable information regarding substrate-catalyst interaction in the transition state
Ø Goal: Develop method to extract this information
Ø Multiple models explored
N
O
HN
2-OMe H2-Me H2-Br H2- iPr H2-OMe Br2-OMe iPr2-OMe Ph
85 (85)88 (90)95 (93)93 (94)93 (92)93 (90)91 (92)92 (89)
substrate % ee
R2
R1
4-Me 78 (77)4-OMe 78 (75)4-NO2 87 (79)
HHH
12345678
entry
91011
R1 R2
H H
NNN
iPr
iPrBr
NNN
iPr
References 1. Mahlau, M.; List, B. Angew. Chem. Int. Ed. 2013, 52, 518.
2. Rauniyar, V.; Lackner, A. D.; Hamilton, G. L.; Toste, F. D. Science 2011, 334, 1681.
3. Neel, A. J.; Hehn, J. P.; Tripet, P. F.; Toste, F. D. J. Am. Chem. Soc. 2013, 135, 14044.
4. Milo, A.; Bess, E. N.; Sigman, M. S. Nature, 2014, 507, 210. 5. Neel, A. J.; Milo, A.; Toste, F. D.; Sigman, M. S. Science 2015,
347, 737. 6. Zi, W.; Wang, Y-M.; Toste, F. D. J. Am. Chem. Soc. 2014, 136,
12864. 7. Martínez-Aguirre, M. A.; Yatsimirsky, A. K. J. Org. Chem.
2015, 80, 4985.
Ø In 2014, Toste and coworkers disclosed a method for the enantioselective fluorination of allylic alcohols via chiral anion phase-transfer catalysis, using aryl boronic acids (BA) as transient directing groups (e.g. B).6
Ø Preliminary survey reveals ability to tune ee over 2.5 kcal/mol range (–78 - 65 % ee) by changing only BA
Ø Library of PAs and BAs prepared to cover large structural space then ee’s obtained for each combination
Ø Degree of asymmetric induction heavily dependent on BA and PA structure- better understanding of their interaction may facilitate improvement of scope.
Ø Dataset trends and model complexity suggest that different mechanisms may be operative depending on PA/BA structure.
Ø Catalyst-dependent nonlinear effect supports this notion.
Ø Catalyst speciation affected by PA/BA structure. Given propensity of phosphate anion to form –ate complex with boronic acids7, a plausible scenario may involve this species.
B OO
Ph
OHPO
OO*
B O
PhPOO
O*O
OH
R
RNN
Cl
FX
NN
Cl
FX
-ate complexH-bonded complex
Ø Current efforts focused on modeling subsets of the data to identify structural features controlling selectivity within a given regime.
M+ –chiral anion
+ X–insoluble cationic
reagent
–+soluble chiral
ion pair
enantioenriched product
prochiralsubstrate+
Ø The Toste group has a long standing interest in the use of chiral anions for asymmetric catalysis.1 Recently, this strategy has been applied in the realm of phase transfer catalysis.2
Ø In 2013, Toste and coworkers reported an enantioselective oxidative coupling reaction enabled by a novel class of triazolyl phosphoric acids (PAs, e.g. 3a).3
NHN
OR1
NNN
R
R1 R2RH H (4a)4-Me4-OMe4-NO22-OMe
2-Br2-iPr
2-Me
2-OMe2-OMe2-OMe2,6-(OMe)2
H (4b)H (4c)H (4d)H (4e)H (4f)H (4g)H (4h)
H (4l)
Ph (4i)Br (4j)iPr (4k)
CH(Ph)2 (3b)2,4,6-(Me)3Ph (3c)2,4,6-(iPr)3Ph (3d)2,4,6-(Cy)3Ph (3e)
1-adamantyl (3a)
Ph (3f)4-OMe-Ph (3g)4-(NEt2)-Ph (3h)4-(SO2Me)-Ph (3i)2,6-(OMe)2-Ph (3j)2,6-(F)2-Ph (3k)
R2
Ø Conclusion: Succeeded in rationally modifying noncovalent interactions to improve an enantioselective reaction5
3,5-(OM
e)2
3,5-Me
2
3,5-(CF3 )2
3- OM
e
3- Me
3- CF3
XX X X X2,6-(CF3 )2
2- OM
e
2,6-Me
2
2-CF3
2- Me
4- tBu4-NO
2 4-Ph
4- OM
e
4-CF3
4-Me
4- Bn4- iBu
B(OH)2
R
4-R'2-R3-R
-1.3 (-80)
1.0 (70)
kcal/mol (% ee)
Ph OHF (S)(R)
Ph
Me
OH
(S)-TRIP ( 10 mol %)Selectfluor (1.3 eq.)Na2HPO4 (4.0 eq.)
boronic acid (1.0 eq.) MS 4Atoluene (0.1 M)rt, 16 h, 500 rpm
Ph OHF
(S)-65
OO
POOH
R
R
BHO OH
R
(7b) 2,4,6-(Me)3(7c) 2-iPr(7d) H(7e) 2,6-(iPr)2(7f) 4-iPr(7g) 3,5-(Me)2(7h) 3,5- (CF3)2
R = 4-Me= 4-NO2= 2-Me= 2-OMe= 3,5-(OMe)2= 3,5-(Me)2= 3,5-(CF3)2
B OHHOB OHHOB OHHO
B OHHO
Me
Me
NO2
MeO
7a7b7c7d7e7f7g7h
6691
29
47
-15-8
721124
55703319
-20
-8 -61
72824
-15-6
-57 -48
41
-28
B OHHO
MeO OMe
B OHHO
Me Me
-7720
2322
1221
-23
-31633742
3431
-1636 38 18 13 -12 10
cat
R =(7a) 2,4,6-(iPr)3
0"
10"
20"
30"
40"
50"
60"
70"
0" 10" 20" 30" 40" 50" 60" 70" 80" 90" 100"
Prod
uct(E
e((%
)(
Catalyst(Ee((%)(
(S)444iPr(
OO
POOH
iPr
iPr
Ph OHF
+ o-tolylboronic acid
7f
(S)-6
0"
10"
20"
30"
40"
50"
60"
70"
0" 10" 20" 30" 40" 50" 60" 70" 80" 90" 100"
Prod
uct(E
e((%
)(
Catalyst(Ee((%)(
(S)4TRIP(
Ph OHF
OO
POOH
iPr
iPr
iPr
iPr
iPr
iPr
+ p-tolylboronic acid
7a
(R)-6
Ø Specific goals: accurately predict new PA/BA outcomes via understanding of underlying mechanism.
PhHO
B ORHO B OOBHO OH
Me Me
(R)-Na-7f(1 equiv)
(1.3 equiv)Ph
OHP
OOO*
benzene-d6
11B NMR: δ = 28.7 ppm
11B NMR: δ = 1.45 ppm
11B NMR: δ = 30.4 ppm
The authors acknowledge the University of California and the Lawrence Berkeley National Laboratory for funding. A.J.N. gratefully acknowledges an Amgen Fellowship in Organic Chemistry for funding and an ACS DOC grant for assistance with travel costs. A.M. would like to acknowledge the Center for High Performance Computing at the University of Utah.
18 examples
up to 94 % eeup to 93% yieldN
HN
OR1
N
N
OR1
triazole PA (5 mol%)
N OAcHN BF4
Na3PO4 (2.4 equiv.)p-xylene, rt, 24 h
R2 R2
NHN
OR1 R
OP
O OO *
via chiral ion pair
catalyst conversion (%) ee (%)1
3a
86
91
8
-84
R2
R2
R2
X NY
pyr-3aimid-3a
88 -4193 -45
entry1
345
Ar =
OO
Ar
Ar
C8H17
C8H17
pyr-3a: X=CH, Y=Nimid-3a: X=N, Y=CH
3a: X=N, Y=N
1: R2 = iPr
2 95 162
2: R2 = Cy
PO2H
R1 = Bn R2 = H
A
OMe
74
82
38
77
43
84
77
75
NO2Me
69
80
49
78
MeO
41
70
82
70
N NN
N NN
Cy
Cy
Cy
N NN
N NN
‡
‡
‡
‡
‡
‡‡
‡
‡
‡
‡
CHPh2
2,4,6-MePh
2,4,6-iPrPh2,4,6-CyPh
1-Ad
Ph4-OMe-Ph
2,6-OMe-Ph
4-HNEt2LPh2,6-F-Ph
4-SO2Me-Ph
0.5 1 1.5 2
0.5
1
1.5
2
Measured DDG‡ HkcalêmolL
PredictedDDG‡Hkca
lêmolL
2-Me-Bn
*"*"*"
*"*"*"*"*"*" N
N
OMe
cat
3h3g3f
ee1
3i
62
33
3a3b
9163
57
23
R2 = 0.95y = 0.95x + 0.05
3j3k 53
63
Torsion Angle = –0.14 - 0.18νN=N + 1.39 νRingD + 0.33 νN=N:νRingD
R2 = 0.91
ΔΔG = 1.06 + 0.36 νN=N - 0.42tor R2 = 0.96
ΔΔG = 1.18 + 0.67 νN=N – 0.61 νRingD – 0.18 νRingD:νN=N
*****
*
NN
N
νN=N
νRing Deformation
R
torsion angle
4f
3d3e
3c 889392
***
Ph
Me
OH
(S)-AdDIP ( 10 mol %)Selectfluor (1.3 eq.)Na2HPO4 (4.0 eq.)
p-tolylboronic acid (1.0 eq.) MgSO4p-xylene/ethylcyclohexane (1:1)
rt, 16 h
Ph O B Ar
O[F]H
PO
O OO
*
H
n Ph OHnF
6, n =1, 72 % yield, 93 % ee n = 2, 48 % yield, 0 % ee
5, n =1
B
n =2
up to 86 % yieldup to 94 % ee
OO
POOH
Ad
Ad
iPr
iPr
iPr
iPr
(S)-AdDIP
15 examples
B(OH)2B(OH)2 B(OH)2B(OH)2
F
FF
F
F
Me Met-Bu
B(OH)2
Me
with (S)-TRIP
35 0 –30 25 50ee of (S)-6
4-Me4-NO22-Me2-OMe
–tor (cat)–B1(cat)
B5 (BA) 2,4,6-Me(7b)
2,4,6-iPr(7a)
AdDIP 2-iPr(7c)
H (7d) 4-iPr
(7f)
2-BA with 2,4,6-cat