DOI: 10.1038/NCHEM.1297 Multidimensional Steric Parameters ... · Matrix form and Y were stepwise...

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SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCHEM.1297

Multidimensional Steric Parameters in the Analysis of Asymmetric Catalytic Reactions

Supporting Information

Kaid C. Harper, Elizabeth N. Bess, Matthew S. Sigman

Table of Contents

General Information S2

Steric Parameters S3

NHK Allylation of Benzaldehyde Data S5

Model Development for the Allylation of Benzaldehyde S6

NHK Allylation of Acetophenone Data S10

Model Development for the Allylation of Acetophenone S11

The Desymmetrization of Bisphenol Data S15

Model Determination for the Desymmetrization of Bisphenols S16

NHK Allylation of Benzaldehyde Data Using the Oxazoline

Proline Ligand Library S20

Attempts at Model Determination for the NHK Allylation of

Benzaldehyde Using the Oxazoline Proline Ligand Library S21

Principal Component Analysis for the Allylation of Benzaldehyde

Using the Oxazoline Proline Ligand Library S25

NHK Propargylation of Acetophenone Data S26

Model Determination for the Propargylation of Acetophenone S27

Principal Component Analysis for the Propargylation of Acetophenone S32

References S33

Multidimensional Steric Parameters in the Analysis of Asymmetric Catalytic Reactions

Supporting Information

Kaid C. Harper, Elizabeth N. Bess, Matthew S. Sigman

Table of Contents

General Information S2

Steric Parameters S3

NHK Allylation of Benzaldehyde Data S5

Model Development for the Allylation of Benzaldehyde S6

NHK Allylation of Acetophenone Data S10

Model Development for the Allylation of Acetophenone S11

The Desymmetrization of Bisphenol Data S15

Model Determination for the Desymmetrization of Bisphenols S16

NHK Allylation of Benzaldehyde Data Using the Oxazoline

Proline Ligand Library S20

Attempts at Model Determination for the NHK Allylation of

Benzaldehyde Using the Oxazoline Proline Ligand Library S21


Using the Oxazoline Proline Ligand Library S25

NHK Propargylation of Acetophenone Data S26

Model Determination for the Propargylation of Acetophenone S27

Principal Component Analysis for the Propargylation of Acetophenone S32

References S33

© 2012 Macmillan Publishers Limited. All rights reserved.



General Information

All data used in this report were previously reported, and the reader is directed to the primary publications for information regarding ligand characterization, ligand synthesis, reaction conditions and other reaction outcomes. Information regarding the Nozaki‐Hiyama‐Kishi allylation of benzaldehyde and acetophenone can be found in SI references 1 &2.1,2 Information regarding the desymmetrization of bis‐phenols can be found in SI references 3 & 4.3,4 Information regarding the 3D library for the Nozaki‐Hiyama‐Kishi reactions can be found in SI reference 5.5 Information regarding the propargylation of ketones can be found in SI reference 6.6




Steric Parameters

Table S1. Literature values for the substituents used in this report.

Substituent Interference (kcal/mol)

A (kcal/mol) Charton Molar

Refractivity Sterimol B1 Sterimol B5

Sterimol L

H 0.956 0 0 0.1 1 1 1.52 Me 9.7275 1.7 0.52 0.57 1.52 2.04 2.87 Et 1.8 0.56 1.03 1.52 3.17 4.11 Ph 7.911 2 0.57 2.54 1.71 3.11 6.28 i‐Pr 12.57 2.1 0.76 1.5 1.9 3.17 4.11 Cy 2.2 0.87 2.67 1.91 3.49 6.17 t‐Bu 18.308 4.4 1.24 1.97 2.6 3.17 4.11

CH2t‐Bu 2 1.34 1.52 4.18 4.89 meIpr 0.98 1.52 4.45 4.92 CHEt2 1.51 2.13 4.01 4.72 1‐Ad 1.33 3.16 3.49 6.17 Cet3 2.38 2.94 4.18 4.92 CHPr2 1.54 1.9 4.54 6.17 Bn 0.7 1.52 6.02 4.62

Mean Value 9.89 2.03 0.73 1.48 1.86 3.57 4.68

Std. Deviation 6.36 1.19 0.43 0.98 0.40 1.19 1.35

Table S1 lists the literature values for the steric parameters found in this report. More extensive lists of parameters can be obtained from References 7‐16.7‐16 For our purposes of comparison, the parameters were normalized according to the definition:

XN = (X – Xave) / sx

Where XN is the normalized value, X is the literature value, Xave is the mean value for the range of X, and sx is the standard deviation for the range in X. Using this equation, the Tables S2 and S3 were obtained, which were reported in the text.

Table S2. Normalization scores for small substituents in several parameters.

Substituent Interference (kcal/mol) A (kcal/mol) Charton Molar

Refractivity H ‐1.405 ‐1.523 ‐1.710 ‐1.417

Me ‐0.026 ‐0.360 ‐0.496 ‐0.935

Et ‐0.338 ‐0.403 ‐0.464

Ph ‐0.312 ‐0.601 ‐0.379 1.083

i‐Pr 0.421 0.016 0.064 0.018

Cy ‐0.338 0.321 1.216

t‐Bu 1.323 0.777 1.185 0.499

CH2t‐Bu ‐0.338 1.418




Table S3. Comparison of Normalized Charton parameters and Sterimol B1 parameters.

Substituent Charton Sterimol B1

H ‐1.710 ‐2.143

Me ‐0.496 ‐0.847

Et ‐0.403 ‐0.847

Ph ‐0.379 ‐0.374

i‐Pr 0.064 0.100

Cy 0.321 0.125

t‐Bu 1.185 1.844

CH2t‐Bu 1.418 ‐0.847

meIpr 0.578 ‐0.847

CHEt2 1.815 0.673

1‐Ad 1.395 3.239

Cet3 3.847 2.691

CHPr2 1.885 0.100

Bn ‐0.076 ‐0.847




NHK Allylation of Benzaldehyde Data

Figure S1. The NHK allylation of benzaldehyde using Oxazoline‐Proline ligand series 1.

Table S4. Data gathered in the NHK allylation of benzaldehyde using conditions in Fig. S1.

X Group Major

Enantiomer Minor

Enantiomer ΔΔG‡ Me 60 40 0.245 Et 66 34 0.382 i‐Pr 82 18 0.913 tBu 96 4 1.854

CH(Pr)2 73 27 0.598 CEt3 92 8 1.411

CH(iPr)2 77 23 0.730 1‐Ad 96 4 1.854




Model De

Tha version The raw dand meas

Table S5.

Matrices XNext, the the prom

Figure S2

Fig. S2 shothe upper

evelopment f

he benzaldehthat containsdata was comsured respons

Matrix form

X and Y werestepwise solvpt window sh

. The stepwis

ows the coeffr right box an

for the Allylat

hyde Sterimols the statisticspiled into nuse, respective

atting of the

Y Group Me Et i‐Pr tBu

CH(Pr)2 CEt3

CH(iPr)2 1‐Ad

input into thver was initiahown in Fig. X

se regression

ficients for ead also indicat

tion of Benza

model was ds toolbox is remeric matriceely, as shown

benzaldehyd

MaB1 1.521.521.92.61.9

2.942.083.16

he Matlab™ soted using theXX.

prompt.

ach term (X1, tes whether e

aldehyde

derived using equired to aces X and Y accin Table S5.

e data.

atrix X B5 L2.04 23.17 43.17 43.17 44.54 64.18 44.19 43.49 6

oftware usinge command “s

X2, and X3 coeach term is (

Matlab™ sofccess the stepcording to lig

MatL ΔΔ2.87 0.24.11 0.34.11 0.94.11 1.86.17 0.54.92 1.44.12 0.76.17 1.8g X = [Matrix Xstepwise(X,Y)

orrespond to(blue) or is no

ftware. The spwise linear regand substitue

trix Y ΔG‡ 2454 3818 9135 8543 5977 4106 7300 8543 X] and Y = [M)”. This comm

o B1, B5, and ot (red) includ

student versioegression tooent paramete

Matrix Y] notatmand produce

L, repsectivelded in the cur

on or ol. ers

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model. Fivalue, as wright box.box is a hclicking ondisplay sh

Figure S3

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Figure S4

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benzaldehyde

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ble S6).

s




Table S6. Comparison of experimentally measured and predicted (from Eq. 1) enantioselectivities for the NHK allylation of benzaldehyde.

Measured ΔΔG‡

Predicted ΔΔG‡

0.245 0.359 0.382 0.359 0.913 0.715 1.854 1.372 0.598 0.715 1.411 1.691 0.730 0.884 1.854 1.897




NHK Allylation of Acetophenone Data

Figure S6. Standard conditions for the NHK allylation of acetophenone.

Table S7. Raw data for the allylation of acetophenone.

Y Group Enantiomer 1 Enantiomer 2 ΔΔG‡ Me 25 75 ‐0.650 Et 24 76 ‐0.680 i‐Pr 38 62 ‐0.300 tBu 69 31 0.470

CH(Pr)2 38 62 ‐0.300 CEt3 62 38 0.280

CH(iPr)2 55 45 0.120 1‐Ad 72 28 0.566




Model Development for the Allylation of Acetophenone

The acetophenone Sterimol model was derived using Matlab™ software. The student version, or a version which contains the statistics toolbox, is required to access the stepwise linear regression tool. The raw data was compiled into numeric matrices X and Y according to ligand substituent parameters and measured response, respectively, as shown in Table S8.

Table S8. Matrix format for the allylation of acetophenone data.

Matrix X Matrix Y Y Group B1 B5 L ΔΔG‡ Me 1.52 2.04 2.87 ‐0.650 Et 1.52 3.17 4.11 ‐0.680 i‐Pr 1.9 3.17 4.11 ‐0.300 tBu 2.6 3.17 4.11 0.470

CH(Pr)2 1.9 4.54 6.17 ‐0.300 CEt3 2.94 4.18 4.92 0.280

CH(iPr)2 2.08 4.19 4.12 0.120 1‐Ad 3.16 3.49 6.17 0.566

Matrices X and Y were input into the Matlab™ software using X = [Matrix X] and Y = [Matrix Y] notations. Then the stepwise solver was initiated using the command “stepwise(X,Y)”. This command prodcued the prompt window shown in Fig. S7.

Figure S7. Initial regression command prompt.




This promrespectiveincluded igives the coefficienin Fig. S7. model calthe mode

Figure S8

mpt shows theely) in the upin the currentintercept valunts in the upp The bottomlculated. By cel to give the d

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ophenone iter

for each termand also indiS7 shows thaselected releWith no termory of the valch term in then in Fig. S8.

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Figure S10

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MeasureΔΔG‡

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on of




The Desymmetrization of Bisphenol Data

Figure S11. Standard conditions for the desymmetrization reaction.

Table S10. Desymmetrization data with replicate runs.

R Group Major

Entantiomer Minor

Enantiomer ΔΔG‡ Me 76 24 0.543 Et 78 22 0.596 Ph 75 25 0.517 Bn 71 29 0.421 iPr 87 13 0.895 tBu 97.5 2.5 1.724 Cy 83 17 0.746

CH2tBu 77.5 22.5 0.582 CHEt2 92 8 1.150 CH2iPr 67 33 0.333 CHPh2 84 16 0.780 1‐Ad 97.5 2.5 1.724 Me 76 24 0.543 Et 78.5 21.5 0.610 Ph 74.5 25.5 0.505 Bn 72.5 27.5 0.456 iPr 86.5 13.5 0.874 tBu 97.5 2.5 1.724 Cy 81.5 18.5 0.698

CH2tBu 77.5 22.5 0.582 CHEt2 91 9 1.089 CH2iPr 68.5 31.5 0.366 CHPh2 86 14 0.854 1‐Ad 97.3 2.7 1.687




Model Determination for the Desymmetrization of Bisphenols

The desymmetrization Sterimol model was derived using Matlab™ software. The student version, or a version which contains the statistics toolbox, is required to access the stepwise linear regression tool. The raw data was compiled into numeric matrices X and Y according to ligand substituent parameters and measured response, respectively, as shown in Table S11.

Table S11. Matrix format for the desymmetrization data.

Matrix X Matrix Y

R Group B1 B5 L ΔΔG‡ Me 1.52 2.04 2.87 0.543 Et 1.52 3.17 4.11 0.596 Ph 1.71 3.11 6.28 0.517 Bn 1.52 6.02 4.62 0.421 iPr 1.9 3.17 4.11 0.895 tBu 2.6 3.17 4.11 1.724 Cy 1.91 3.49 6.17 0.746

CH2tBu 1.52 4.18 4.89 0.582 CHEt2 2.13 4.01 4.72 1.150 CH2iPr 1.52 4.45 4.92 0.333 CHPh2 2.01 6.02 5.15 0.780 1‐Ad 3.16 3.49 6.17 1.724 Me 1.52 2.04 2.87 0.543 Et 1.52 3.17 4.11 0.610 Ph 1.71 3.11 6.28 0.505 Bn 1.52 6.02 4.62 0.456 iPr 1.9 3.17 4.11 0.874 tBu 2.6 3.17 4.11 1.724 Cy 1.91 3.49 6.17 0.698

CH2tBu 1.52 4.18 4.89 0.582 CHEt2 2.13 4.01 4.72 1.089 CH2iPr 1.52 4.45 4.92 0.366 CHPh2 2.01 6.02 5.15 0.854 1‐Ad 3.16 3.49 6.17 1.687

Matrices X and Y were input into the Matlab™ software using X = [Matrix X] and Y = [Matrix Y] notations. The stepwise solver was initiated using the command “stepwise(X,Y)”. This command produced the prompt window shown in Fig. S12.

Figure S12. Initial stepwise regression prompt.




This promrespectivein the curintercept upper righ

With no thistory ofterm in thFig. S13.

Figure S13

mpt shows theely) in the uprent model. value, as welht box.

erms includef the values ofhe upper right

3. Initial inclu

e coefficients per right box Fig. S12 showll as selected

d in the modef the root‐met hand box, e

usive model f

for each termand also indi

ws that no terrelevant stat

el, these statean‐square erach term is in

for the desym

m (X1, X2, andicates whetherms are includistics for the

istics are mearror (RMSE) oncorporated i

mmetrization

d X3 correspoer the term isded in the momodel given

aningless in Fof each modeinto the mod

reaction.

ond to B1, B5s (blue) or is nodel. The cenby the blue c

Fig. S12. The l calculated. Bel to give the

, and L, not (red) inclunter box givescoefficients in

bottom box iBy clicking one display show

uded s the n the

s a n each wn in




In Fig. S14present mterms. Thvalues arestatistics sby the “N

Figure S14

4, note that amodel. The mohe coefficiente subjected toshow that theext Step” ind

4. First and f

ll the terms aodel history nts are estimato t‐ and p‐tese B5 term is iicator). Rem

inal iteration

are shown in bnow has 4 poited for the B1ts to measurensignificant aoval of this te

of the desym

blue, which mints for the se1, B5 and L tee their signifiand should beerm leads to t

mmetrization

means that aleparate inclusrms in the upcance in the e eliminated fthe model sh

model.

l terms are insion of each opper right hanmodel at hanfrom the modhown in Fig. S

ncluded in theof the three nd box. Thesend. These del (as promp14.

e

e

pted




Exclusion of the B5 term leads to the model with the highest degree of statistical significance shown in Eq. 3.

ΔΔG‡ = ‐0.417 + 0.928 B1 – 0.110 L (3)

Using this model, predictions were calculated for all the synthetic substrates, generating the data in Table S12.

Table S12. Experimentally measured values compared to those given by Eq. 3 for the desymmetrization reaction.

Measured ΔΔG‡

Predicted ΔΔG‡

0.543 0.680 0.596 0.544 0.517 0.483 0.421 0.488 0.895 0.897 1.724 1.547 0.746 0.681 0.582 0.458 1.150 1.044 0.333 0.455 0.780 0.885 1.724 1.841 0.543 0.680 0.610 0.544 0.505 0.483 0.456 0.488 0.874 0.897 1.724 1.547 0.698 0.681 0.582 0.458 1.089 1.044 0.366 0.455 0.854 0.885 1.687 1.841




NHK Allylation of Benzaldehyde Data Using the Oxazoline Proline Ligand Library ***

Figure S15. Standard conditions for the allylation of benzaldehyde.

For the complete data set, refer to previously published work.6




Attempts at Model Determination for the NHK Allylation of Benzaldehyde Using the Oxazoline Proline Ligand Library

Because the data analyzed herein was generated prior to our conception of applying modeling techniques to determine an optimized ligand, only those data points representing an evenly spaced data spread were included in the matrices for model determination. Table S13 presents all experimental ΔΔG‡values, averaged over two runs, and Sterimol parameters for the substituent combinations evaluated. Substituent combinations highlighted in yellow are those used for model determination. Table S13. Full data set for NHK allylation of benzaldehyde.

Substituent Combination a,b XB1 XB5 XL YB1 YB5 YL

Measured ΔΔG‡

HM 1 1 2.06 1.52 2.04 2.87 0.0000 HE 1 1 2.06 1.52 3.17 4.11 0.3004 HI 1 1 2.06 1.9 3.17 4.11 0.2541 HB 1 1 2.06 2.6 3.17 4.11 0.9410 HD 1 1 2.06 1.9 4.54 6.17 0.6464 HT 1 1 2.06 2.94 4.18 4.92 0.1640 MM 1.52 2.04 2.87 1.52 2.04 2.87 0.1308 ME 1.52 2.04 2.87 1.52 3.17 4.11 0.2427 MI 1.52 2.04 2.87 1.9 3.17 4.11 0.3239 MB 1.52 2.04 2.87 2.6 3.17 4.11 0.8602 MD 1.52 2.04 2.87 1.9 4.54 6.17 1.0808 MT 1.52 2.04 2.87 2.94 4.18 4.92 0.3598 EM 1.52 3.17 4.11 1.52 2.04 2.87 0.2427 EE 1.52 3.17 4.11 1.52 3.17 4.11 0.5816 EI 1.52 3.17 4.11 1.9 3.17 4.11 0.8796 EB 1.52 3.17 4.11 2.6 3.17 4.11 0.8226 ED 1.52 3.17 4.11 1.9 4.54 6.17 1.1342 ET 1.52 3.17 4.11 2.94 4.18 4.92 0.1308 IM 1.9 3.17 4.11 1.52 2.04 2.87 0.2771 IE 1.9 3.17 4.11 1.52 3.17 4.11 0.8796 II 1.9 3.17 4.11 1.9 3.17 4.11 0.9626 IB 1.9 3.17 4.11 2.6 3.17 4.11 1.2891 ID 1.9 3.17 4.11 1.9 4.54 6.17 0.6164 IT 1.9 3.17 4.11 2.94 4.18 4.92 0.0217 BM 2.6 3.17 4.11 1.52 2.04 2.87 0.2290 BE 2.6 3.17 4.11 1.52 3.17 4.11 0.1974 BI 2.6 3.17 4.11 1.9 3.17 4.11 0.3299 BB 2.6 3.17 4.11 2.6 3.17 4.11 0.3358 BD 2.6 3.17 4.11 1.9 4.54 6.17 0.4214

a First letter is X substituent. Second letter is Y substituent. b Substituent codes: H= ‐H, M= ‐CH3, E= ‐CH2CH3, I= ‐CH(CH3)2, B= ‐C(CH3)3, D= ‐CH(C3H7)2, T= ‐C(C2H5)3 Selection of substituent combinations for inclusion in the model data set for model determination was based upon variation in B5. This selection criterion was chosen due to B5 exhibiting the largest spread of




values. When B5 values were similar, the decision for inclusion of a substituent combination in the modeling data set was based upon the degree of spread in B1 values, as L values increase in a fashion similar to the increase seen in B5. Inclusion of all possible crossterms resulted in matrices X and Y (Table S14).

Table S14. Matrix format for the allylation data.

Matrix X Matrix Y

XB1 XB5 XL YB1 YB5 YL XB1YB1 XB1YB5 XB1YL XB5YB1 XB5YB5 XB5YL XLYB1 XLYB5 XLYL ΔΔG‡

1.00 1.00 2.06 1.52 2.04 2.87 1.52 2.04 2.87 1.52 2.04 2.87 3.13 4.20 5.91 0.0000

1.00 1.00 2.06 1.90 3.17 4.11 1.90 3.17 4.11 1.90 3.17 4.11 3.91 6.53 8.47 0.2541

1.00 1.00 2.06 2.60 3.17 4.11 2.60 3.17 4.11 2.60 3.17 4.11 5.36 6.53 8.47 0.9410

1.00 1.00 2.06 2.94 4.18 4.92 2.94 4.18 4.92 2.94 4.18 4.92 6.06 8.61 10.14 0.1640

1.52 2.04 2.87 1.52 2.04 2.87 2.31 3.10 4.36 3.10 4.16 5.85 4.36 5.85 8.24 0.1308

1.52 2.04 2.87 1.90 3.17 4.11 2.89 4.82 6.25 3.88 6.47 8.38 5.45 9.10 11.80 0.3239

1.52 2.04 2.87 2.60 3.17 4.11 3.95 4.82 6.25 5.30 6.47 8.38 7.46 9.10 11.80 0.8602

1.52 2.04 2.87 2.94 4.18 4.92 4.47 6.35 7.48 6.00 8.53 10.04 8.44 12.00 14.12 0.3598

1.90 3.17 4.11 1.52 2.04 2.87 2.89 3.88 5.45 4.82 6.47 9.10 6.25 8.38 11.80 0.2771

1.90 3.17 4.11 1.90 3.17 4.11 3.61 6.02 7.81 6.02 10.05 13.03 7.81 13.03 16.89 0.9626

1.90 3.17 4.11 2.60 3.17 4.11 4.94 6.02 7.81 8.24 10.05 13.03 10.69 13.03 16.89 1.2891

1.90 3.17 4.11 2.94 4.18 4.92 5.59 7.94 9.35 9.32 13.25 15.60 12.08 17.18 20.22 0.0217

2.60 3.17 4.11 1.52 2.04 2.87 3.95 5.30 7.46 4.82 6.47 9.10 6.25 8.38 11.80 0.2290

2.60 3.17 4.11 1.90 3.17 4.11 4.94 8.24 10.69 6.02 10.05 13.03 7.81 13.03 16.89 0.3299

2.60 3.17 4.11 2.60 3.17 4.11 6.76 8.24 10.69 8.24 10.05 13.03 10.69 13.03 16.89 0.3358

Matrices X and Y were input into the Matlab™ software using X = [Matrix X] and Y = [Matrix Y] notations. Then, the stepwise solver was initiated using the command “stepwise(X,Y)”. This command produced the prompt window shown in Fig. S16.




Figure S16

This promX14, and XXLYB1, XLYnot (red) center bocoefficienmeaningleof each m

6. Initial Regr

mpt shows theX15 correspoYB5, and XLYL, included in thox gives the innts in the uppess in Fig. S16

model calculat

ression Comm

e coefficients ond to terms Xrespectively) he current montercept valueer right box. 6. The bottomted.

mand Prompt.

for each termXB1, XB5, XL, YBin the upper odel. Fig. S16e and other reWith no termm box is a his

.

m (X1, X2, X3,1, YB5, YL, XB1Yright box and6 shows that elevant statisms included intory of the va

, X4, X5, X6, XYB1, XB1, YB5, Xd also indicatno terms are stics for the mn the model, alues of the r

X7, X8, X9, X1B1, YL, XB5YB1, es whether e included in tmodel given bthese statistioot‐mean‐sq

0, X11, X12, XXB5, YB5, XB5YLeach is (blue) the model. Thby the blue cs are uare error (R

X13, L, or is he

RMSE)




Fitting a linear relationship to this data set proved problematic. The data matrix (Matrix X) contains 15 variables and 15 data points. Thus, the starting model for backward elimination stepwise regression (which includes all terms in the initial model) suffered from over‐fitting. Alternatively, forward selection stepwise regression was considered. However, as seen in Fig. S16, there were no terms that could be included in the model to increase the model fit.

Attempts at modeling via partial least squares (PLS) regression highlighted the problematic covariance amongst the Sterimol parameters (Table 3), as well as additional confounding aspects of the data set, which remain under investigation.





The principal component analysis was performed in Matlab™ from Matrix X (X) using the commands:

[coefs, scores, variances]=princomp(X) percent_explained = 100*variances/sum(variances)




NHK Propargylation of Acetophenone Data

Figure S17. Standard conditions for the QuinPro ligand library evaluation in the NHK propargylation reaction.

Table S15. Data, with replicate runs, obtained from the ligand library evaluation.

Electronic Group (E)

Steric Group (S) Peak 1 Peak 2 ΔΔG‡

CF3 Me 44.6 55.4 ‐0.1270 CF3 tBu 50.6 49.4 0.0141 CF3 CEt3 60 40 0.2375 H Me 53 47 0.0704 H tBu 86.8 13.2 1.1034 H CEt3 76.4 23.6 0.6882

OMe Me 60.2 39.8 0.2424 OMe tBu 90.8 9.2 1.3413 OMe CEt3 79.9 20.1 0.8085 CF3 Me 47 53 ‐0.0704 CF3 tBu 62 38 0.2868 CF3 CEt3 62 38 0.2868 H Me 55 45 0.1176 H tBu 87.5 12.5 1.1401 H CEt3 78.8 21.2 0.7692

OMe Me 60 40 0.2375 OMe tBu 92 8 1.4309 OMe CEt3 82 18 0.8884




Model Determination for the Propargylation of Acetophenone

The propargylation Sterimol model was derived using Matlab™ software. The student version, or a version which contains the statistics toolbox, is required to access the stepwise linear regression tool. The raw data was compiled into numeric matrices X and Y according to ligand substituent parameters and measured response, respectively, as shown in Table S16. Matrix X differs from previous like matrices because the data set contains variation in steric and electronic parameters. Thus, Matrix X includes crossterms between the electronic parameter and each of the Sterimol parameters. The crossterms are derived by multiplying the respective parameters for a given ligand.

Table S16. Matrix format for the raw propargylation data.

Matrix X Matrix Y

B1 B5 L σ σB1 σB5 σL ΔΔG‡ 1.52 2.04 2.87 0.54 0.8208 1.1016 1.5498 ‐0.127042.6 3.17 4.11 0.54 1.404 1.7118 2.2194 0.014062

2.94 4.18 4.92 0.54 1.5876 2.2572 2.6568 0.237551.52 2.04 2.87 0 0 0 0 0.0703892.6 3.17 4.11 0 0 0 0 1.103422

2.94 4.18 4.92 0 0 0 0 0.6882431.52 2.04 2.87 ‐0.27 ‐0.4104 ‐0.5508 ‐0.7749 0.2424362.6 3.17 4.11 ‐0.27 ‐0.702 ‐0.8559 ‐1.1097 1.341323

2.94 4.18 4.92 ‐0.27 ‐0.7938 ‐1.1286 ‐1.3284 0.8085331.52 2.04 2.87 0.54 0.8208 1.1016 1.5498 ‐0.070392.6 3.17 4.11 0.54 1.404 1.7118 2.2194 0.286812

2.94 4.18 4.92 0.54 1.5876 2.2572 2.6568 0.2868121.52 2.04 2.87 0 0 0 0 0.1175672.6 3.17 4.11 0 0 0 0 1.14005

2.94 4.18 4.92 0 0 0 0 0.7691961.52 2.04 2.87 ‐0.27 ‐0.4104 ‐0.5508 ‐0.7749 0.237552.6 3.17 4.11 ‐0.27 ‐0.702 ‐0.8559 ‐1.1097 1.430898

2.94 4.18 4.92 ‐0.27 ‐0.7938 ‐1.1286 ‐1.3284 0.888383Matrices X and Y were input into the Matlab™ software using X = [Matrix X] and Y = [Matrix Y] notations. The stepwise solver was initiated using the command “stepwise(X,Y)”. This command produced the prompt window shown in Fig. S18.

Figure S18. Initial regression command prompt.




This promσ, σB1, σBnot (red) center boblue coeffmeaningleof each mincorpora

Figure S19

mpt shows theB5, and σL, reincluded in thox gives the inficients in theess in Fig. S18

model calculatated into the m

9. Initial inco

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orporation of

for each termn the upper riodel. Fig. S18e as well as sebox. With nom box is a hisng on each tere the display s

all variables i

m (X1, X2, X3,ght box and i8 shows that elected releva terms includtory of the varm in the uppshown in Fig.

in the regress

, X4, X5, X6, aindicates wheno terms are ant statistics ded in the moalues of the rper right hand S19.

sion.

and X7 corresether each te included intofor the modeodel, these staoot‐mean‐sqd box, each te

pond to B1, Brm is (blue) oo the model. el given by theatistics are uare error (Rerm is

B5, L, or is The e

RMSE)




In Fig. S19present mThe coeffbox. Theshand. Thiprompted

Figure S20

Fig. S20 i

Figure S2

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Figure S22

This mode

Eq. 4 wasmeasure

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Table S17. Experimentally measured values compared to values predicted from Eq. 4.

Measured ΔΔG‡

Predicted ΔΔG‡

‐0.127 ‐0.178 0.014 0.198 0.238 0.300 0.070 0.130 1.103 1.024 0.688 0.676 0.242 0.284 1.341 1.437 0.809 0.864 ‐0.070 ‐0.178 0.287 0.198 0.287 0.300 0.118 0.130 1.140 1.024 0.769 0.676 0.238 0.284 1.431 1.437 0.888 0.864




Principal Component Analysis for the Propargylation of Acetophenone

The principal component analysis plot of this data was generated in Matlab™ from Matrix X (X) using the commands:

[coefs, scores, variances]=princomp(X) percent_explained = 100*variances/sum(variances)




References

(1) Miller, J. J.; Sigman, M. S. Angew. Chem., Int. Ed. 2008, 47, 771. (2) Sigman, M. S.; Miller, J. J. J. Org. Chem. 2009, 74, 7633. (3) Lewis, C. A.; Gustafson, J. L.; Chiu, A.; Balsells, J.; Pollard, D.; Murry, J.; Reamer, R. A.; Hansen, K.

B.; Miller, S. J. J. Am. Chem. Soc. 2008, 130, 16358. (4) Gustafson, J. L.; Sigman, M. S.; Miller, S. J. Org. Lett. 2010, 12, 2794. (5) Harper, K. C.; Sigman, M. S. Science 2011, 333, 1875. (6) Harper, K. C.; Sigman, M. S. Proceedings of the National Academy of Sciences 2011, 108, 2179. (7) Bott, G.; Field, L. D.; Sternhell, S. J. Am. Chem. Soc. 1980, 102, 5618. (8) Adams, R.; Yuan, H. C. Chem. Rev. 1933, 12, 261. (9) Winstein, S.; Holness, N. J. J. Am. Chem. Soc. 1955, 77, 5562. (10) Hansch, C.; Leo, A. Exploring QSAR: Fundamentals and Applications in Chemistry and Biology;

American Chemical Society: Washington, DC, 1995. (11) Charton, M. J. Am. Chem. Soc. 1975, 97, 3691. (12) Charton, M. J. Org. Chem. 1976, 41, 2217. (13) A. Verloop, J. T. In Bological Activity and Chemical Structure; Buisman, J. A., Ed.; Elsevier:

Amsterdam, 1977, p 63. (14) A. Verloop, J. T. In QSAR in Drug Desing and Toxicology; D. Hadzi, B. J.‐B., Ed.; Elsevier:

Amsterdam, 1987, p 97. (15) Verloop, A. In Drug Design; Ariens, E. J., Ed.; Academic Press: New York, 1976; Vol. III, p 133. (16) Verloop, A. In IUPAC Pesticide Chemistry; Miyamoto, J., Ed.; Pergamon: Oxford, 1983; Vol. 1, p

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DOI: 10.1038/NCHEM.1297 Multidimensional Steric Parameters ... · Matrix form and Y were stepwise...

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Transcript of DOI: 10.1038/NCHEM.1297 Multidimensional Steric Parameters ... · Matrix form and Y were stepwise...