Quantifying Structural Effects of Amino Acid Ligands in Pd(II

Nov 21, 2017 - Delineating complex ligand effects on enantioselectivity is a longstanding challenge in asymmetric catalysis. With α-amino acid ligand...
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Quantifying Structural Effects of Amino Acid Ligands in Pd(II)Catalyzed Enantioselective C−H Functionalization Reactions Yoonsu Park,†,‡ Zachary L. Niemeyer,§ Jin-Quan Yu,∥ and Matthew S. Sigman*,§ †

Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea Center for Catalytic Hydrocarbon Functionalizations, Institute for Basic Science (IBS), Daejeon 34141, Republic of Korea § Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States ∥ Department of Chemistry, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States ‡

S Supporting Information *

ABSTRACT: Delineating complex ligand effects on enantioselectivity is a longstanding challenge in asymmetric catalysis. With α-amino acid ligands, the essential difficulty lies in accurately describing integrated perturbations induced by simultaneous variation about the α side chain and N protecting group of the ligand, which hampers an intuitive understanding of the structure−enantioselectivity relationships. To deconvolute such complexity in chiral amino acid enabled enantioselective C−H functionalization reactions, a computational organometallic model system was developed. Whereas a model based only on a conventional results in diminished predictive power, the ground state Pd(II)-based models display an excellent ability to describe the observed enantioselectivity. These structures were leveraged using a multivariate modeling approach to successfully describe Pd(II)-catalyzed C−H alkylation, alkenylation, and two C−H arylation reactions, wherein descriptors of torsion angle, percent buried volume, and NBO charge showed quantitative relevance to predict enantiomeric excess. On the basis of the insights revealed in these case studies, an optimal set of amino acid ligands is suggested to provide maximum information in a screening campaign.

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INTRODUCTION Amino acids are widely recognized as a versatile ligand class in organometallic catalysis.1 As an N,O-bidentate donor, amino acid ligands have considerable effects on the stereoelectronic nature of the resultant metal complex. Importantly, chiral amino acids combined with the appropriate metal provide the framework for the development of enantioselective processes. Indeed, such processes have been disclosed in various catalytic systems. For example, Cu(II)-catalyzed enantioselective Diels− Alder reactions2 have been reported in which a key π−π interaction between the amino acid ligand and the substrate is proposed as a feature for effective asymmetric induction. Additionally, Ru-,3 Rh-,4 and Cr-based5 complexes of amino acids effectively catalyze asymmetric transfer hydrogenation of prochiral ketones to produce chiral alcohols.6 These examples highlight the effectiveness of amino acid ligands in stereoselective catalysis. In this context, a suite of Pd-catalyzed enantioselective C−H functionalization reactions7 of aromatic C(sp2)−H or aliphatic C(sp3)−H bonds has been developed by applying a wide range of monoprotected amino acids (MPAA, Scheme 1).8 By fine tuning both the α side chain and N substituent on these ligands, the Yu group has identified conditions for C−H alkylation,9 olefination,10 arylation,11 and iodination12 with high levels of enantioselectivity. Hypotheses of the role of the chiral © XXXX American Chemical Society

scaffold resulted in the group recently developing new classes of chiral ligands including chiral hydroxamic acids,13 aminoethyl quinolines,14 and aminomethyl oxazolines.15 Concurrent with the reaction development have been extensive studies into the key mechanistic manifold for inducing asymmetric induction via experimental and theoretical methods (vide infra).16 However, structural effects of the amino acid ligand on the reaction outcome have been less explored until recently.17 In essence, the key questions are as follows. (1) What interactions from the amino acid are necessary to achieve high enantiomeric excess? (2) How does the variation about the α substituent and N protecting group affect the active species relevant for the C−H bond cleavage, often the assumed (or computationally predicted) enantiodetermining event? By addressing these fundamental mechanistic questions, a more rational evaluation of ligands can be enabled as more reactions utilizing these simple and modular ligands are investigated. Herein, we describe such a study utilizing multivariate modeling of the observed enantioselectivity with molecular descriptors to develop correlations revealing the key structural features responsible for effective asymmetric catalysis. This was accomplished by employing computationally derived (MPAA)Received: October 9, 2017

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DOI: 10.1021/acs.organomet.7b00751 Organometallics XXXX, XXX, XXX−XXX

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Organometallics Scheme 1. α-Amino Acid Ligands in Pd(II)-Catalyzed Enantioselective C−H Functionalization

Scheme 2. Reaction Mechanism and Model Selection

PdII complexes to identify the key parameters, which were then successfully applied to four unique enantioselective C−H functionalization reactions (Scheme 1). These include (a) C(sp2)−H alkylation of diphenyl(2-pyridyl)methane,9 (b) C(sp2)−H olefination of α,α-diphenylacetic acid,10 and (c) C(sp3)−H arylation of cyclopropanes11 containing two different directing groups. On the basis of the conclusions disclosed in this study, a designer screening set of amino acid ligands is suggested that is expected to provide maximum information with regard to structure−selectivity relationships in evaluating new reactions utilizing this ligand class. Additionally, the applied descriptors can be easily obtained for new-generation ligands for future predictions and virtual screening campaigns.

This information provides the infrastructure to initiate an investigation of structure−enantioselectivity relationships using physical organic tools recently developed in the Sigman lab.21 In this approach, statistical treatment of DFT-derived structural parameters enables the construction of a regression model capable of describing enantioselectivity. Most strikingly, a robust model can then be used to virtually predict the performance of untested ligand structures. The workflow for this procedure is to first decide on the types of surrogate structures that provide the most information at the lowest computational cost while key structural features from the mechanistic studies are maintained.22 As the geometry of the MPAA is rigidified upon coordination to the metal center, we hypothesized that the choice of an accurate conformation would be critical in parametrization of ground state structures that best imitate the key C−H cleavage step. By use of this computation-based approach, the virtual evaluation of ligands without synthesizing all possible candidates is enabled.23 However, this technique relies on proper parameters extracted from a relevant model system so as to accurately describe the free energy differences between two putative transition states serving as a divergence point along a single mechanistic manifold.21a With this in mind, we initially envisioned two possible structural model compounds that potentially satisfy these needs: the first model, which is referred to as the Ligand Model in Scheme 2b, consists of neutral amino acids having a noncovalent 4N−H···1O interaction. The geometries are fully optimized with the M06-2X/aug-cc-pVTZ level of theory, which has been shown to have excellent correlations in parametrization.22b,24 Although this type of structure is not

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RESULTS AND DISCUSSION Reaction Mechanism and Model Selection. Extensive studies on the mechanism of the Pd(II)/MPAA system have suggested that the key step inducing enantioselectivity lies in stereoselective cyclometalation of prochiral substrates to afford organometallic Pd(II) species, as depicted in Scheme 2a. Kinetic studies on catalytic C−H olefination concluded that the amino acid ligands would suppress the formation of higher order Pd acetate species and bind to the Pd(II) center in a bidentate manner,18 while a recent study implied that involvement of dimeric Pd species cannot be completely ruled out.18b Notably, DFT calculations on the related systems indicate that the active species relevant for H atom abstraction from a C−H bond of the substrate would possess dianionic coordination, thus providing a chiral environment about the monomeric metal center.19 Very recently, Musaev and coworkers reported a study exploring transition state ligand effect analysis of C(sp2)−H olefination, highlighting the importance of rigid bidentate coordination on the chirality induction (vide infra).17 Further mass spectroscopic analysis and computational investigation revealed that the N protecting group in the dianionic (MPAA)PdII complex is likely involved in the deprotonation process by acting as an internal base.20 B

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Organometallics necessarily the most stable conformer,25 we speculated that the hydrogen bonding may retain the distance between 1O and 4N, so that the molecular structure would resemble the organometallic transition state. A second model, which is referred to as the PdII Model in Scheme 2b, contains a square-planar Pd(II) complex bearing a dianionic MPAA and two ethylene molecules as placeholder ligands.26 Ethylene is particularly useful in this context, as it provides the simplest dative neutral bond via η2 coordination, thus maintaining a 16-electron complex and potentially mimicking substrate binding. As this model has dianionic coordination of MPAA to the Pd(II), steric and electronic information about the metal center can be interrogated. The ground-state geometries are computed with the B3LYP-D3BJ/[6-31G(d,p)+Lanl2dz(Pd)] level of theory, which has been effective for describing Pd/MPAA catalysis.17,20c On the basis of these considerations, ground state geometries of representative MPAAs were computationally optimized and evaluated. Not surprisingly, a systematic comparison of the Ligand Model and the PdII Model revealed significant differences in the geometric signatures. As enumerated in Scheme 2b, the calculated structures from the PdII Model contain a significantly more planar backbone, in comparison to the Ligand Model (the torsion angle for the acid backbone, φ1−2−3−4, was −14° versus −34°). The torsion angle between the backbone plane and substituent R (φ1−2−3−6) is also 19° higher with the PdII Model. Most interestingly, relative values between these two model systems poorly correlate to each other (Scheme 2c). This implies that the geometries of the Pd Model are not simply a linear transformation (e.g., rotation matrix multiplication) from the structures of the Ligand Model. These changes are presumably due to multifaceted interactions between the substituents and the metal center, leading us to speculate that structural information resulting from each model would be distinctive. Parameter Selection. As the next step, molecular descriptors from the two models were collected and representatives are depicted in Figure 1. To interrogate steric effects, torsion angles related to the carboxylic acid backbone (φ1−2−3−4), α side chain (φ1−2−3−6, φ6−3−4−5), and nitrogen protecting groups (φ2−3−4−5) were measured. Sterimol values were tabulated for both the α substituent and N protecting group as a strategy to consider both maximum and minimum steric influence.22a,27 As potential electronic descriptors,

computed charges using natural bond orbitals (NBO) for different atoms were considered. In addition, vibrational frequencies and their corresponding intensities for carbonyl stretches were examined to potentially describe integrated effects of the mass and strength of the bond.28 In the PdII Model, parameters related to the Pd atom were additionally gathered. For example, torsion angles including the Pd atom (φ1−Pd−4−5, φ6−3−4−Pd) and the NBO charge of the metal center (NBOPd) were included. Moreover, percent buried volume (%Vbur) of the Pd atom was compiled as a quantitative descriptor for ligand shielding29 by utilizing numerically derived atomic radii.30 A comprehensive list of parameters and their covariance analysis are summarized in the Supporting Information. Multivariate Analysis: Case Studies. Having established the structural model systems for parametrization, we first examined the enantioselective C(sp2 )−H alkylation of diphenyl(2-pyridyl)methane, which was reported by the Yu group in 2008.9 Stereoselective desymmetrization of this prochiral substrate was successfully achieved by applying Nprotected amino acids as the chiral ligand. We examined this particular reaction, as it constitutes one of the pioneering examples demonstrating catalytic and enantioselective C−H functionalization using Pd catalysis. Moreover, as depicted in Figure 2, extensive structural variations on the amino acids and their responses on the enantioselectivity were available for data mining, both facilitating the statistical analysis and providing the prospect of deconvoluting and probing the effects of the α substituents and the N protecting groups independently. Recently, extensive transition state analysis was performed to understand this transformation, further enabling the evaluation of the effectiveness of multivariate analysis under a well-defined mechanistic scaffold.17,20b As an initial step in building statistical models, an analysis for single variable parameter correlations was executed for descriptors extracted from both the Ligand and the PdII Models. Variations about the α substituent were first assessed by evaluating the tert-butyloxycarbonyl (Boc)-protected MPAA ligands (Figure 2a). An ee of 90% was achieved with isobutyl substitution (L5), whereas the values were significantly reduced with isopropyl (L3) or tert-butyl (L6) substitutions. Within this subset, a quantitative correlation was revealed comparing the torsion angle of the ligand backbone (φ 1−2−3−4) in the PdII Model and the observed enantioselectivity (R2 = 0.84, Figure 2a, top graph). Widening the 1O−2C−3C−4N torsion angle displayed a clear tendency to enhance enantioselectivity. Considering that φ1−2−3−4 is highly correlative with the torsion angle between the R group and the Pd center (φ6−3−4−Pd, Figure S3 in the Supporting Information), the quantitative relevance of φ1−2−3−4 and observed ee can be regarded as a direct illustration of steric interaction between the side chain and the metal center. Interestingly, addition of %Vbur to the single-parameter model significantly increases the robustness of the correlation, as described in Figure 2, bottom graph (R2 = 0.97, Q2LOO = 0.95). The two-parameter model suggests that large shielding of the amino acid ligand is an important factor in achieving high enantioselectivity. As both torsion angle and percent buried volume are generally considered purely steric descriptors, it can be concluded that the variation about R largely affects the metal center by modulating the molecular geometry of the Pd(II) complex. It is intriguing that only moderate structure−activity relationships were identified with the Ligand Model. A

Figure 1. Potential descriptors for the Pd/MPAA system. C

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Figure 2. Normalized structure−activity relationship of enantioselective C−H alkylation. (a) Effect of α side chain variation. (b) Effect of N protecting group variation. Legend to footnotes: (a) enantiomeric excess data wereobtained from Shi et al.,9 and measured ΔΔG⧧ values were calculated from ΔΔG⧧ = −RT ln(er) at 298.15 K (eq S1 in the Supporting Information); (b) parameters were not normalized.

univariate correlation was found with φ1−2−3−4 (R2 = 0.76, Figure S5 in the Supporting Information), but further improvement of the model was not possible using the acquired parameters. These results imply that the descriptors derived from the PdII Model are likely better at reflecting mechanistic features encoded in the enantioselective C−H cleavage process. The effect of N protecting groups on the enantioselectivity was subsequently examined by fixing the α-substituent as the isobutyl group. As shown in Figure 2b, carbamate protection generally gives rise to high levels of enantioselectivity from 79% to 90% ee, whereas N-pivaloyl (L12) or N-formyl groups (L14) deliver poorer enantioselectivity (7% and 6% ee, respectively). Notably, the N-acetyl moiety (L13) yields an unexpectedly high enantiomeric excess (80% ee), despite not containing an oxycarbonyl moiety. Using the same strategy as above, a simple correlation using natural bond orbital charge of the Pd atom (NBOPd) was found (R2 = 0.87), implying that a more electrophilic metal center results in increased selectivity. NBO charges of the carbonyl oxygen of the N protecting group also result in a univariate correlation (Figure S4 in the Supporting Information), albeit with a modestly reduced correlation coefficient, suggesting the functional role of the oxygen atom acting as an internal base (vide infra). Further analysis revealed a model including vibrational parameters of the carbonyl stretch: vibrational frequency (νCO) and its associated IR intensity (iCO) displayed a significant correlation (Figure 2b). Considering that the motion of this stretch is related to the abstraction of the substrate C−H bond in the proposed transition state,19,20 it is reasonable to assume that this model is describing the relevant electronic influences of the protecting group. Most pleasingly, it appears that the parameters in the PdII Model are describing the key characteristics from both the R group substitution and the protecting group. Having established two individual effects of the key modular components of MPAA ligands, a global model capable of predicting simultaneous variations on both positions was

developed using the PdII Model descriptors. To our delight, linear regression analysis identified a model that solely utilized the descriptors previously recognized in accounting for individual effects of the α substituent and N protecting group (Figure 3).

Figure 3. Normalized regression model for the global set.

A linear combination of dihedral angle of the acid backbone (φ1−2−3−4), NBO charge of the Pd (NBOPd), and a cross term between charges for the Pd and the carbonyl oxygen at the protecting group (NBOPd × NBOCO) predicts 16 reaction outcomes (R2 = 0.93, Figure 3). As the coefficients of the normalized parameters are illustrative of the quantitative contribution of the variables to the model, the torsion angle and the NBO charge of the Pd can be regarded as the most influential factors: the larger torsion angle and enhanced electrophilicity of the metal center are beneficial in improving enantioselectivity. The interaction term having an NBO charge of the carbonyl oxygen possibly takes into account the basicity of the ligand while simultaneously correcting for other unaccounted interactions. Statistical examination of the robustness of the model via a leave-one-out (LOO) cross-validation D

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Figure 4. Linear regression model for (a) C−H alkenylation, and (b) C−H arylation reactions. Legend to footnotes: (a) the enantiomeric excess data were obtained from Shi et al.;10 (b) the enantiomeric excess data were obtained from Chan et al.;11b (c) parameters were not normalized, as only a single variable was used.

displayed a minimal drop in Q2 value (0.87). Further evaluation of the model using a k-fold cross-validation method also showed the robustness, despite the limited data in the training set (k = 3, Q2av = 0.75, Table S12 in the Supporting Information). Again, the Ligand Model failed to deliver a predictive or robust model (Figure S7 in the Supporting Information).31 To further validate the Pd II model system, other enantioselective C−H functionalization reactions with extended variations in the amino acid ligands were probed. Specifically, two reactions were selected: an enantioselective C−H alkenylation of α,α-diphenylacetic acid10 and C−H arylation of cyclopropane.11b Results from these transformations were subjected to similar analyses of both the α side chain and protecting group effects. As enumerated in Figure 4a, enantioselectivity of the C−H olefination tends to increase as the size of the R substituent increases, as represented by methyl (L1, 54% ee), isopropyl (L3, 93% ee), and tert-butyl (L6, 94% ee) variation. Univariate regression analysis revealed that the torsion angle of the acid backbone (φ1−2−3−4) is again correlative with two outliers (Figure S8 in the Supporting Information). Notably, this parameter was previously identified as a descriptor for the side chain variation of the C−H alkylation of diphenyl(2-pyridyl)methane, as shown in Figure 2a. While the same descriptors are effective in describing both reactions, the signs and extents of the coefficient for φ1−2−3−4 are different: a positive coefficient (+0.08, Table S16 in the Supporting Information) was identified with the C−H olefination, whereas the coefficient for the C−H alkylation reaction was negative (−1.15, Figure 2a). This result implies that similar structural features for each system are not universal, and these interactions uniquely affect enantioselectivity in the course of catalysis. The changing coefficient highlights that the ligand can have similar interactions with the substrate that produce distinct outcomes. In essence, the stereochemistry imbued by the ligand is unique to the specific reaction and the substrate. A qualitative representation of this effect is observed in comparing methyl (L1) to tert-butyl (L6) substitution, as

this dramatically lowered the ee from 80% to 52% in the alkylation reaction (Figure 2a), whereas the same variation enhanced selectivity for the olefination reaction from 54% to 94% ee. Although the univariate model delivered a qualitative understanding of the side chain variation, a few ligands, such as L4 and L5, did not follow the same trend (Figure S8 in the Supporting Information). This suggested that complex interactions would exist during catalysis, hampering an intuitive interpretation of the ligand effects. Further stepwise regression disclosed a more sophisticated relationship accounting for all reaction outcomes in a predictive manner (Figure 4a). Specifically, torsion angles related to the position of the Ncarbonyl group (φ1−Pd−4−5 and φ6−3−4−5) gave a correlative model (R2 = 0.87, Q2LOO = 0.75). The necessity of these angles is likely a direct consequence of interactions between the α side chain and protecting group. For example, the φ1−Pd−4−5 value reflects a repulsive interaction between the α substituent and protecting group as a large side chain may be geared with the protecting group, reducing the torsion angle.20b Methyl (L1) to tert-butyl (L6) substitution decreases this angle from 158° to 142° (Table S3 in the Supporting Information). The φ6−3−4−5 value is also perturbed by a similar interaction. As these angles reflect the position of the N-carbonyl group, which engages in enantioselective C−H palladation,19,20 the importance of the parameters in this model is not surprising. Most intriguingly, the statistical modeling technique is capable of capturing mechanistic features involved in the key transformation. In order to analyze the description of the N protecting group, C(sp3)−H arylation of cyclopropane11b was further examined (Figure 4b). Utilizing a trifluoromethylsulfonyl (Tf)-protected amine as the directing group, enantioselective functionalization of cyclopropane C−H bonds was disclosed with various Nprotected amino acids. Analysis of these reaction outcomes revealed that a single parameter, the NBO charge of the carbonyl oxygen, correlates well with the enantioselectivity (Figure 4b). This relationship demonstrates that an increase in negative charge on the oxygen results in higher enantioselectivity. This correlation again likely indicates that the oxygen E

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Figure 5. Multivariate analysis on C−H arylation of cyclopropane. Measured ee data were obtained from Wasa et al.11a TcBoc = 2,2,2-trichloro-tertbutyloxycarbonyl.

regression analysis, and the full set the ligands is given in the Supporting Information. We established a training set comprising 13 ligands, which is organized to properly reflect the range in variation about both positions (Figure 5b). Multivariate analysis revealed that three parameters resulted in an excellent quantitative correlation (R2 = 0.93, Figure 5c and Table S19 in the Supporting Information). These parameters are NBO charge of carbonyl oxygen (NBOCO), vibrational carbonyl stretching frequency (νCO), and percent buried volume (%Vbur). Intriguingly, these descriptors have already been proven to be effective in the aforementioned transformations: NBOCO and νCO were related to the variation of N protecting group, whereas %Vbur was a descriptor for the α side chain variation (vide supra). Applying partial least-squares (PLS) regression corroborated that those descriptors are important in enantioselectivity.31 The high level of goodness-of-fit criteria corroborate the idea that the parameters extracted from the PdII Model are applicable to describing Pd/MPAA catalysis. The robustness of the model was examined by an LOO and k-fold cross validation method (Q2LOO = 0.81, Q2av = 0.81 for k = 3, respectively).31 Furthermore, predictability was examined by the other 13 ligands not in the training set as cross validations (Figure 5c, red diamonds). As depicted in Figure 5c and Table S21 in the Supporting Information, the external set is well predicted with selectivities from 12 ligands within an error of 0.30 kcal/mol. The ligand L37 from the validation set is relatively poorly predicted (0.40 kcal/mol difference), likely due to the large structural variation between this ligand and the training set. Most importantly, as highlighted in Figure 5c, extrapolation of the model enables the prediction of the best-performing ligands L33−L36 within 0.21 kcal/mol absolute error, despite this class of N protecting group, 2,2,2-trichloroethoxycarbonyl groups with extended alkyl chains, being excluded from the training set.31 Those ligand structures are listed in Figure 5d: predicted ΔΔG⧧ values are well matched with measured values. This

atom mediates the C−H bond cleavage by acting as an internal base, 20a and the thermodynamic driving force of the deprotonation step is closely related to the relative kinetics of the selectivity-determining step. Notably, two outliers in this regression analysis were identified as the N-trifluoromethylcarbonyl (L24) and N-pivaloyl group (L26). These substituents have acute steric or electronic features, respectively, that are likely unaccounted for in this simple correlation, resulting in a poor fit with other substituents.22a,32 Although we are not able to entirely describe the underlying origin of these outliers using the parameters included in the multivariate analysis, changes in mechanism could be operative for the deprotonation step. Specifically, trifluoromethyl substitution dramatically reduces proton affinity of the carbonyl oxygen leading to the hypothesis that an acetate anion could function as the exogenous base to deprotonate the C−H bond.20c In a similar vein, the size of the tert-butyl group may alter the active species through an interaction with the isopropyl group installed at the α side chain. This in turn could affect the conformation of the amino acid precluding intramolecular deprotonation. Despite these issues, stereoelectronic parameters derived from the PdII model are capable of describing key features of the amino acid ligand that affect the enantioselectivity, and predictions from this model system are consistent with experimental observations. As a final case study, a reaction with extensive and simultaneous variations on both the α side chain and N protecting group was sought. Of paramount interest with respect to the future of applying these parametrization tools to facilitate ligand screening is to utilize a limited training set to define the model and subsequently predict and extrapolate an external set of ligands. An example reaction that fits these criteria is the enantioselective C−H arylation of cyclopropanes reported in 2011,11a which is assisted by an electron-deficient amide directing group (Figure 5a). In this report, extensive variations on the amino acid ligands were examined. In total 26 ligands and their enantioselectivities were subjected to the F

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model clearly displays the high potential of utilizing the PdII Model parameters as a tool to rationally design new ligands for a given transformation. Suggestion of a Screening Set. Considering the broad applicability of MPAA ligands to asymmetric catalysis, efficient evaluation of reaction parameters is of paramount importance. A series of case studies presented herein indicated that specific parameters extracted from the PdII Model are particularly effective for describing enantioselectivity induced by the Pd/ MPAA system. For example, torsion angle or NBO charge are capable of adequately describing structural variations in univariate correlations. These intuitive relationships lead us to consider what an optimal screen set of ligands would encompass. Variation would include a set of two ligand substitution patterns, which may statistically provide maximum information with regard to structure−enantioselectivity relationships (Scheme 3). To explore the influence of the side

CONCLUSIONS In summary, development of a computationally Pd(II) based parameter system has enabled the construction of predictive models for amino acid based asymmetric transformations, whereas conventional free ligand modeling resulted in poor correlations. Four enantioselective C−H functionalization reactions were successfully interrogated by applying DFTderived molecular descriptors, such as torsion angle of the acid backbone, percent buried volume, and NBO charges. These parameters proved to be widely applicable to distinctive reactions, suggesting that the stereoelectronic information encoded in the enantioselectivity-determining transition state is well-reflected in the developed model system. It is interesting to compare the parameters derived in our first case study to a DFT-based transition state analysis, very recently reported by Musaev and co-workers.17 The report includes detailed conformational analysis on the Pd complex bearing the diphenyl(2-pyridyl)methane substrate, revealing that the α substituent on the ligand is potentially interacting with the substrate to enhance the formation of the major enantiomer. This subtle interaction was likely captured in our parameterbased analysis as %Vbur in Figure 2. We believe this illustrates the complementary nature of multivariate modeling and transition state analysis. Transition state analysis can be difficult to perform accurately on new reaction systems, which is often limited by mechanistic understanding and computational power. However, the output can provide exquisite detail. Conversely, multivariate modeling is readily applied to new reaction systems once an adequate parameter structural inventory is identified. The statistical analysis can be performed regardless of prior knowledge about the mechanistic manifold. However, the parameters ultimately found in the mathematical relationship are indicative of plausible interactions, priming future physical organic studies.35 Therefore, each of these methods can be used constructively to serve individual goals in future studies. Within the current work, we have suggested a set of ligands that can be used to construct multivariate models for future diverse reaction development. This will serve to limit the necessary experimental efforts in reaction screening, while providing clues as to the role of these ligands. Further parameter development as well as optimization campaigns with related ligands are currently underway in our laboratories.

Scheme 3. Suggestion of a Ligand Set

chain efficiently, we suggest testing five ligands, which include tert-butyl, isopropyl, benzyl, methyl, and isobutyl substitutions. These ligands span a broad range of φ1−2−3−4 as seen in Scheme 3a, which one would expect to interrogate the relative importance of the α side chain. Similarly, the effect of the protecting group can be estimated by analyzing five ligands comprising Boc, TcBoc, Fmoc, acetyl, and formyl groups (Scheme 3b). These substitutions were selected to include significant variation about the NBO charges for the carbonyl oxygen (NBOCO), as this parameter has repeatedly proven to produce a useful univariate correlation. As a final step, by applying design-of-experiment precepts to the amino acid ligands, the resulting 5 × 5 ligand matrix revealed by these suggestions can be further reduced to a face-centered cubic design matrix,21a,33 which includes 13 ligands in total (Scheme 3c). This rather modest sized training set would be particularly well-suited for multivariate analysis in an efficient manner, as it contains evenly distributed data points about the plausible structural variations. Of note, most of these suggested ligands are also commercially available, further enabling the screening process.34 Also, it is likely that any screening campaign of a ligand with the expanse of structural variance possible that these encompass would require at least this level of evaluation in the optimization effort.

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ASSOCIATED CONTENT

* Supporting Information S

Full list of ligands, computational details, statistical modeling, and Cartesian coordinates of geometry optimized structures. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.organomet.7b00751.

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AUTHOR INFORMATION

Corresponding Author

*E-mail for M.S.S.: [email protected]. ORCID

Jin-Quan Yu: 0000-0003-3560-5774 Matthew S. Sigman: 0000-0002-5746-8830 G

DOI: 10.1021/acs.organomet.7b00751 Organometallics XXXX, XXX, XXX−XXX

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Organometallics Notes

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Jing-Yao Guo (University of Utah) and Dr. Gang Chen (The Scripps Research Institute) for helpful discussions. Y.P. thanks Prof. Sukbok Chang (KAIST) and the Institute for Basic Science (IBSR10-D1) in Korea for financial support. Z.L.N, J.-Q.Y., and M.S.S. thank the National Science Foundation under the Center for Chemical Innovation in Selective C−H Functionalization (CHE-1205656) for financial support.

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DOI: 10.1021/acs.organomet.7b00751 Organometallics XXXX, XXX, XXX−XXX