Developing Comprehensive Computational Parameter Sets To

7 (6), pp 4144–4151. DOI: 10.1021/acscatal.7b00739. Publication Date (Web): May 5, 2017. Copyright © 2017 American Chemical Society. *E-mail fo...
0 downloads 0 Views 2MB Size
Subscriber access provided by Eastern Michigan University | Bruce T. Halle Library

Article

Developing Comprehensive Computational Parameter Sets to Describe the Performance of Pyridine-Oxazoline and Related Ligands Jing-Yao Guo, Yury Minko, Celine B. Santiago, and Matthew S Sigman ACS Catal., Just Accepted Manuscript • Publication Date (Web): 05 May 2017 Downloaded from http://pubs.acs.org on May 5, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

ACS Catalysis is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 10 1 2 3 4 5 6 7 8 9 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Catalysis

Developing Comprehensive Computational Parameter Sets to Describe the Performance of Pyridine-Oxazoline and Related Ligands Jing-Yao Guo, Yury Minko, Celine B. Santiago and Matthew S. Sigman* Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States ABSTRACT: The applicability of computational descriptors extracted from metal-pyridine oxazoline complexes to relate both site and enantioselectivity to structural diversity was investigated. A group of computationally derived features (e.g., metal NBO charges, steric descriptors, torsion angles) were acquired for a library of pyridine-oxazoline ligands. Correlation studies were employed to examine steric/electronic features described by each descriptor, followed by application of the said descriptors in modeling the results of two reaction types: the site-selective redox-relay Heck reaction, and the enantioselective Carroll rearrangement, affording simple, well-validated models. Through experimental validation and extrapolation, parameters derived from ground state metal complexes were found to be advantageous over the free ligand.

KEYWORDS parameterization, rational ligand design, selective oxidation, ground state metal complex.

Ligand optimization in a metal-catalyzed process is a necessity in modern reaction development. To facilitate this type of campaign, modularity of ligand structure to access variation of ligand substituents coupled with intuitiondriven empirical optimization is the most common approach applied.1 As such, our group has recently endeavored to streamline this process by integrating data-driven tools to correlate structural descriptors to various reaction outputs (Scheme 1).2-3 This technique provides both mechanistic rationale3b, 3g-i, 4 and a mathematical relationship to enable virtual screening3g, 5 with the purpose of ultimately improving reaction performance. The most important step in the sequence is to define the key set of parameters that concisely describe the initial empirical outputs employed for model construction, i.e., the training set, with high accuracy. Thus, an underlying goal of our recent efforts is to identify descriptive features for ligands commonly applied in a wide range of catalyst development efforts.3c, 3g, 3j, 6 Herein, we present the investigation of salient parameters that describe pyridine oxazolines, a common bidentate ligand class especially in oxidative catalysis.7 The new parameters were vetted in two reaction types: the enantio- and site selective redox-relay Pd-catalyzed Heck reaction7i and the enantioselective Ru-catalyzed Carroll rearrangement8. Additionally, we introduce new algorithms to facilitate both the organization of parameters as well as the determination of the best models describing reaction outputs (see SI for details). In terms of context, pyridine-oxazoline (PyrOx) and quinoline-oxazoline (QuinOx) ligands have recently received increased attention as chiral ligands, since they can be applied in a wide range of oxidative processes in which phosphines are unlikely to be compatible.9 Additionally, the ligands can be prepared in a modular manner from

commercially available substituted pyridine carboxylic acids (or nitriles), and -amino acid derivatives. The preparation generally requires only two synthetic steps and several of the ligands are now commercially available.10 Finally, the distinct electronic features of its two nitrogen coordination sites have facilitated their application in reactions that are poorly promoted by similar ligand types including bipyridines.11 Considering these factors, the goal of this study is the development of a comprehensive set of parameters, which describes the key features of these ligands, empowering training set design as well as providing insights for future applications of pyridine oxazolines.

Scheme 1. General workflow of model development.

Ligand-Dependent Site Selectivity in Enantioselective Redox-Relay Heck Reactions. Our group has developed a suite of unusual enantioselective Heck reactions of internal alkenes, which are relatively non-biased electronically (Scheme 2A).7b, 7i, 9, 12 A key feature of this reaction is the ACS Paragon Plus Environment

ACS Catalysis 1 2 3 4 5 6 7 8 9 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

site selectivity of initial migratory insertion (Scheme 2B), as the alkenes are not polarized significantly. Thus, a question, which we have yet to interrogate, is the ligand effects on this process that ultimately results in constitutional isomers of distal (-isomer) and proximal (-isomer) arylation products. In contrast, the site selectivity of the migratory insertion as a function of substrate has been thoroughly investigated both experimentally and computationally.13 Initial empirical observations revealed that the nature of the alkene and arylboronic acid counterparts both have a pronounced effect on site selectivity.3f For example, electron deficient arylboronic acids tend to undergo -insertion, which afford high site selectivity, based on quantitative correlations with both Hammett parameters7i and computationally derived Hammett surrogates accounting for proximal/remote steric effects apart from electronic features.3f, 3j As for the alkenol substrates, increasing the distance between the alkene and the hydroxyl group results in reduced selectivity, suggesting an electronic effect.3h, 7i Additionally, a qualitative trend of the alkene substituent size is observed with larger groups favoring the -addition of the aryl group across the carbon-carbon double bond.3f

Page 2 of 10

relating ligand/catalyst structural features to site selectivity, several observations were considered including: 1) the PdPyrOx complex is most likely formed before alkene coordination and migratory insertion, and 2) this ancillary ligand does not dissociate from palladium during the chain walking sequence.13a These findings suggest that a multivariate correlation analysis based on molecular descriptors obtained from calculated surrogate catalyst structures should be highly informative.3o, 14 This includes the incorporation of the metal center in order to parameterize the ground state transition metal. Additionally, a comparison of descriptors acquired from metal complexes to those extracted from the free ligand structures was undertaken. As we had previously reported, the free ligand can be used to develop correlations for ligand-modulated enantioselectivitiy5a, but we reasoned that more precise descriptions of the structure function relationships may result using the metalligand structures.3l, 3o, 14b

All free ligand structure optimizations in this study were performed at the M06-2X15/def2-TZVP16 level of theory, consistent with the one employed in our previous studies.5a The related metal complexes were calculated at the [B3LYP-D3]17/[LanL2DZ + 6-31G(d,p)]18 level of theory with a smaller basis set for the purpose of saving time and computational resources. The model complexes incorporated two chloride atoms as the anionic ligands on palladium to provide a neutral Pd(II) species in a square planar geometry.

Scheme 2. Site selectivity of the redox-relay Heck reaction.13b As our group has a considerable interest in continuing to exploit this reaction manifold, understanding the effect of ligand structure on site selectivity is prudent. Of particular long term interest is improving the observed site selectivity for more difficult cases, which may require the design of an entirely new ligand platform but would be enabled by understanding how the structural components of pyridine oxazoline ligands influence site selectivity. To initiate a study

Figure 1. Ligand-based molecular descriptors. Free Ligand Derived Parameters. Simulated infrared spectra have been extensively studied and considered a

ACS Paragon Plus Environment

Page 3 of 10 1 2 3 4 5 6 7 8 9 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Catalysis

reliable source of molecular descriptors, providing information on both charge distributions and directional steric interactions.3f, 3g, 3j, 6 The vibrational modes of PyrOx ligands were scrutinized. As a result, four pyridine ring stretches along with the carbon-nitrogen double bond stretch on the oxazoline ring were selected as representative vibrational modes residing on the pyridine and oxazoline moieties, respectively (Figure 1A). The natural bond orbital (NBO) charges are computationally derived atomic charges based on natural population analysis, which require much less computational resources than vibrational descriptors, and have proven to be valuable electronic descriptors.19 In this specific case, the NBO charges of the two ligating nitrogen atoms were initially investigated (Figure 1B). Sterimol parameters L, B1, and B5 provide a metric for steric characteristics, with L describing the length of the substituent along the direction of the bond axis, B1 and B5 defined as the minimum and maximum widths perpendicular to the primary bond, respectively.3c, 20 In the case of PyrOx ligands, the oxazoline substituent was interrogated to provide a steric profile for analysis (Figure 1B). Metal Complex-Derived Parameters. Ground state structures of the individual metal complexes were calculated and compared with the free ligand structures. We hypothesized that these metal complexes bear higher similarity to the active catalytic species and allow access to a series of unique features describing the hybrid electronic/steric interplay of these organometallic intermediates.21

sphere that is centered on the metal atom and was inspired by the seminal work of Tolman.22 Table 1. Performances of training/validation ligand sets on the site-selective Heck reaction.

Figure 2. Metal complex-based parameters. The metal NBO charges and the metal-ligand bonding orbital energies can both represent inclusive catalyst electronic features, in contrast with the free ligand electronic parameters. (Figure 2A). The Sterimol B1 and B5 parameters for the oxazoline substituents were recorded as steric descriptors, while L, being collinear with B5, was considered to be unnecessary. The two sets of Sterimol parameters obtained independently from free ligands and complexes show near perfect agreement with each other (Figure 2B). In addition, the percent buried volume (%Vbur) of metal centers can also reflect the ligand sterics (Figure 2C). Introduced by Nolan and coworkers, the %Vbur is calculated based on the volume occupied by a ligand in an abstract

a Yields

were determined by 1H NMR using CH2Br2 as internal standard. b γ:β ratios were determined by GC analysis. Results are an average value of duplicate reactions.

Furthermore, additional structural features, including bond lengths, bond angles, and torsion (dihedral) angles, are capable of reflecting electronic and/or steric characteristics at varying degrees, and were considered as meaningful descriptors for catalyst assessment.3l, 3o, 14b, 14d, 21, 23 In this study, a selection of descriptors was obtained during parameterization: the lengths of metal-oxazoline nitrogen bonds (DPd-N(ox)), oxazoline ring torsions (φOx torsion), pyridine-oxazoline torsions (φPyr-Ox), metal out-of-plane torsions (φmetal), and metal-ligand bite angles (Figure 2D).

ACS Paragon Plus Environment

ACS Catalysis 1 2 3 4 5 6 7 8 9 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 10

man)

Design of Ligand Series. A training set consisting of sixteen ligands was designed with the aim of achieving lowest sampling bias, demonstrating an even distribution of ligand features across a large range. Taking into consideration the PyrOx ligand backbone structure, the oxazoline substituent modification is primarily imparting ligand steric effects, while the pyridine substituent modification is mainly contributing to the ligand electronic influences. A separated group of ligands, some of which being significantly different from those in the training set, was employed as external validations to verify the robustness and applicability of the derived models24 (Table 1). Correlation Analysis. Having both experimental results and computed parameters, we performed correlation analysis for the training set to investigate the relationships between descriptors, as well as examining univariate trends in observed site selectivity.25 Apart from identifying primary ligand effects, such analysis aids the parameter selection for the following multivariate stage of modeling. It is reported that significant collinearity between parameters is an indicator for description of overlapping molecular properties.26 Consequently, in search of concise models, such descriptors would be mutually exclusive. Furthermore, insights into the exact features described by each parameter can be acquired through benchmarking undefined parameters against well-studied ones. Following this approach, we were able to generate the correlation matrix for selected parameters plus the empirical results, represented as a colormap in Figure 3.

and Sterimol parameters, with correlation coefficient (R) values ranging from 0.72 to 0.86 (Figure 3, see box A). This observation can presumably be explained by the electronic influence originating from the oxazoline substituents. In contrast, parameters describing general or pyridinecentered electronic features (NBOPd, Ebonding, νasym1, and NBON(pyr)) are essentially decoupled with steric parameters (R < 0.05 with all Sterimol parameters), while showing strong inter-correlations at the same time (|R|, the absolute values of R, are above 0.7, Figure 3, see box B), thus being more favorable as indicators of electronic influences. It is presumed that hybrid descriptors (DPd-N(ox) and φOx torsion) extracted from the metal complexes can subsequently be inspected based on their correlations with independent steric and electronic factors. Indeed, obtained results show that DPd-N(ox) is moderately correlated to both factors (|R| has the range of 0.64~0.85 with Sterimol parameters, and 0.30~0.50 with electronic parameters), suggesting a true hybrid nature, while φOx torsion is apparently under pure steric control (range of |R| with steric parameters: 0.87~0.96, inverse correlation caused by the negative values of torsion angles; |R| < 0.05 with electronic parameters, (see Figure 3 box C).

Figure 4. Representative univariate trends.

Figure 3. Correlation colormap. The first column corresponds to the single-parameter correlations of the reaction site selectivities, while the others represent inter-parameter correlations. Strong correlations were found between localized oxazoline electronic descriptors (namely, NBON(ox) and iC=N, Ra-

Apart from scrutinizing the descriptors, the corresponding univariable correlations of the previously mentioned parameters with the experimental results (measured ΔΔGǂ) served as valuable trends for preliminary modeling purposes. For example, a significant steric dependence is identified wherein an increase in size of the oxazoline substituent enhances site selectivity, as illustrated by the significant correlation with all of the Sterimol parameters (R > 0.8). As expected, the correlation with Sterimol B1 emerges among all steric descriptors (Figure 4A), since the free-rotating substituent would position the least bulky side, described by B1, towards the substrate minimizing repulsive interactions. In comparison, the electronic effect appears to be modest as indicated by the poor correlation of site selectivity with independent electronic parameters (NBOPd, Ebonding, νasym1, and NBON(pyr), range of |R|: 0.28~0.38, Figure 4B). As suggested by our previous findings, the electron defi-

ACS Paragon Plus Environment

Page 5 of 10 1 2 3 4 5 6 7 8 9 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Catalysis

ciency of the ancillary ligand leads to higher selectivity, i.e., the electron deficient palladium tends to be delivered to the more electronegative β carbon.3j, 13b Strong push-pull type systems arising from ligands with relatively electronrich oxazoline nitrogens and electron-poor pyridine nitrogens were also found to be advantageous as demonstrated by the significant correlation of site selectivity with the difference in pyridine and oxazoline nitrogen NBO charges (ΔNBON(pyr)-N(ox), Figure 4C). This observation agrees with the previously reported computational mechanistic analysis from Wiest and coworkers.13b Of particular note, it was pleasing to determine that the length of the palladium-oxazoline nitrogen bond (DPd-N(ox)), a hybrid parameter possessing a similar ratio of steric/electronic dependency on the site selectivity as discussed above, provides a strong single-parameter correlation (Figure 4D). This superior performance demonstrates the ability of describing the interacting steric and electronic effects by a simple structural feature, specifically the elongation of a bond, which presumably represents both repulsive interactions of the oxazoline substituent and binding propensity of the corresponding nitrogen. However, when we evaluate the full set of data in Table 1, outliers appear in all single-parameter correlations (Table S5), suggesting that a multivariate study is required to describe the combined steric/electronic influences of more diverse ligands on site selectivity. Linear Regression Analysis. With the evaluation of the parameter set achieved, we performed multivariate linear regression as a second step. Both external validations and cross-validations (especially leave-one-out, LOO, method27 where each response in the set is predicted by model generated from the remainder of the dataset, see SI for details) were then conducted to test the reliability of the resulting preliminary models. The model performances were evaluated by the goodness of fit, the ability to validate and predict, and the interpretability of model parameter components.24, 28 From both ligand- and complex-derived parameter databases, simple predictive models were successfully constructed, each with two independent parameters describing electronic and steric effects, respectively (Figure 5). Two of the comprehensive models exhibit combinations of the corresponding trends identified in the initially performed univariate correlation analysis. In general, γ:β site selectivity is positively correlated to the size of the oxazoline substituent, represented by B1, and negatively correlated to more electron donating pyridines, illustrated by either an increased frequency of an asymmetric pyridine ring stretch (νasym1) or a lower palladium NBO charge (NBOPd). As the models were acquired from normalized descriptors,29 the resulting coefficients can indicate the significance of the represented effects. With the independency of the parameters shown by the lack of inter-correlation as discussed above, the site selectivity was confirmed to be regulated mainly by oxazoline substituent size and modestly influenced by electronic effects.

Figure 5. A) Comprehensive ligand-based model. B) Complex-based model. C) Results of structural optimization for ligand CH2-2-Nap/3F (Table 1, entry 29): free ligand vs. metal complex. Comparing the performance of the various models, it was found that the ligand-derived model is slightly better in terms of the statistical measures. However, it fails when validating two ligands with a 3-substituted pyridine moiety (Figure 5A, corresponding data: Table 1 & Table S8, entries 29, 30). The interaction between the substituent on 3-C of the pyridine and the oxazoline oxygen significantly increases the NPyr=C–C=NOx dihedral angle, making them considerably different from the corresponding structures in the model catalysts, which are nearly planar (Figure 5C). In comparison, the model constructed from the palladium complex-based descriptors is robust to this type of structural changes (Figure 5B). It should be noted that ligands with 3-substitution were found to afford high enantioselectivity in a recently reported dehydrogenative Heck reaction,5a implicating the need to include these ligands in a parameterization platform. Furthermore, an extrapolation point was successfully anticipated by the metal complex-

ACS Paragon Plus Environment

ACS Catalysis 1 2 3 4 5 6 7 8 9 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

derived model, which the ligand-based model failed to predict (Figure 5A &B. Table 1 & Table S8, entry 22. Two outlier tests, the Grubbs’ test30 and Dixon’s Q-test31, were performed to detect model failures. See SI for details). With virtual screening being one of the key purposes of these correlations, the model performance in the region of high selectivity is of great importance. Evidently, the complex-derived model succeeded in providing accurate predictions, while the ligand-derived model had critical failures. This outcome demonstrates the structural advantage of metal-ligand complexes over free ligands in this case study. Further Validation of the Complex-Derived Descriptors. Inspired by the observed results related to the PyrOx-Pd complexes in the parameterization for the redoxrelay Heck reaction, we selected to further evaluate this parameter set by interrogating its applicability in other catalyst systems. We reasoned that the identity of metal in the computed complex structure is not crucial, considering it acts merely as a readout handle for the ligand properties.32 To support this hypothesis, evaluating the reported studies employing pyridine-oxazoline ligands in transition metal-promoted asymmetric catalysis led us to examine the ruthenium-catalyzed Carroll rearrangement (Table 2) as a reasonably extensive dataset has been reported previously. Rendered enantioselective by Tortoioli and Lacour, the Carroll rearrangement can be promoted by both PyrOx and pyridine-imine ligands (PyrIm), with the catalyst adopting a tetrahedral configuration around the metal. However, facial selectivity diverges as a function of the different ligand backbones.8a, 8b Fascinated by this phenomenon, we aimed to explain these results with our library of computed palladium-ligand complexes as the source of descriptors. The reported enantioselectivity data were divided into training and validation sets (Table 2), then subjected to the correlation-regression modeling process using parameters derived from palladium complexes (Figure 6). As a result of the considerable ligand structural variations, including the incorporation of pyridine-indanol structures and the conformational flexibility of the PyrIm backbone, Sterimol parameters were found not to be effective in describing this system. Alternative steric parameters, torsion angle of the oxazoline or imine moieties (φfragment2) and the percent buried volume (%Vbur), were consequently utilized to describe the observed effects. A relatively strong steric effect (|R|(φfragment2) = 0.79, |R|(%Vbur) = 0.39. As the %Vbur describes the percentage area covered by the ligand on a metal-centered sphere, it could be less representative of the ligand steric impact on the substrate, due to the difference in orientation) and moderate electronic effect (|R|(NBOPd) = 0.49) were identified. We were pleased to find that significant univariate correlations could be obtained despite the changes in active metal center and the configuration of the corresponding complex (Figure 6A, circled descriptors). The best correlated single parameter, the NBO charge of the nitrogen on the oxazoline or imine fragment (NBON2) both provided decent trends within each series and significant overall correlation

Page 6 of 10

(Figure 6B). The former observation suggests a negative correlation of enantioselectivity with increased electron density, which might also account for the steric influence of the oxazoline/imine moiety based on the significant correlation of NBON2 with the substituent size. As for the latter, the overall binary value of this parameter corresponds to directionality of the electron push-pull of the bidentate PyrOx and PyrIm ligands. In other words, the electron density on the oxazoline nitrogen is greater than that of the pyridine nitrogen atom, which in turn is higher than that of the imine, making it an efficient classifier for the divergent facial selectivity induced by the two ligand types. Table 2. Results of the enantioselective Carroll rearrangement reported by Tortoioli and Lacour.8a, 8b

a Values in parenthesis are ΔΔGǂ. Results are the average of at least two runs. b Reaction time: 20 h. c 13 h. d 92 h. e 22 h. f 24 h. g 2 h. h 3.5 h. i 1.5 h. j 4 h.

The comprehensive model (Figure 6C) was built around the best single descriptor, with two more parameters applied as correction terms: the steric parameter φfragment2, and the torsion of the metal complexation unit (φMetal), which presumably accounts for both the steric repulsion and the electron density around the binding area. The encouraging accuracy of this model suggests that the metal complex-

ACS Paragon Plus Environment

Page 7 of 10 1 2 3 4 5 6 7 8 9 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Catalysis

derived parameters essentially describe the ligand features and are highly consistent among different metal species, which provides the basis for further application.

and was utilized in the analysis of reaction outputs of two distinct systems: the palladium-catalyzed site selective redox-relay Heck reaction and the enantioselective Carroll rearrangement. Highly descriptive models were acquired through a multidimensional modeling approach. The palladium complex-derived parameter set demonstrated superior performance in comparison to the free ligand-derived set of descriptors. Furthermore, extending its applicability towards ruthenium complexes demonstrates its effectivity regardless of the identity of the active metal species. We envision the descriptor set reported here can be applied to any optimization campaign utilizing ligands of this type and we are actively applying these approaches in a diverse array of Heck type reactions.

Experimental procedures, characterization data, preliminary results, modeling methods, and MATLAB scripts (pdf) Tables of computational data (xlsx)

*Email: [email protected] Figure 6. Results of modeling studies on the rutheniumcatalyzed enantioselective Carroll rearrangement using Pd complex derived descriptors: A) correlation colormap for the training set, B) univariate correlation with NBON2, entire dataset included, and C) comprehensive model.

The authors declare no competing financial interest.

This research was supported by the NSF (CHE1361296). The support and resources from the Center for High Performance Computing at the University of Utah are gratefully acknowledged.

In summary, a library of electronic, steric or hybrid parameters were obtained from palladium-PyrOx complexes

(1) (a) Sigman, M. S.; Jacobsen, E. N. J. Am. Chem. Soc. 1998, 120, 4901-4902; (b) Ding, K.; Ishii, A.; Mikami, K. Angew. Chem. Int. Ed. 1999, 38, 497-501; (c) Reetz, M. T. Angew. Chem. Int. Ed. 2002, 41, 1335-1338; (d) Collins, K. D.; Gensch, T.; Glorius, F. Nat. Chem. 2014, 6, 859-871; (e) Kutchukian, P. S.; Dropinski, J. F.; Dykstra, K. D.; Li, B.; DiRocco, D. A.; Streckfuss, E. C.; Campeau, L.-C.; Cernak, T.; Vachal, P.; Davies, I. W.; Krska, S. W.; Dreher, S. D. Chem. Sci. 2016, 7, 2604-2613. (2) (a) Maldonado, A. G.; Rothenberg, G. Chem. Soc. Rev. 2010, 39, 1891-1902; (b) Wagner, N.; Rondinelli, J. M. Front. Mater. 2016, 3, 1-9. (3) (a) Sigman, M. S.; Harper, K. C.; Bess, E. N.; Milo, A. Acc. Chem. Res. 2016, 49, 1292-1301; (b) Milo, A.; Neel, A. J.; Toste, F. D.; Sigman, M. S. Science 2015, 347, 737743; (c) Harper, K. C.; Bess, E. N.; Sigman, M. S. Nat. Chem. 2012, 4, 366-374; (d) Neel, A. J.; Hilton, M. J.; Sigman, M. S.; Toste, F. D. Nature 2017, 543, 637-646; (e)

Bess, E. N.; Bischoff, A. J.; Sigman, M. S. Proc. Natl. Acad. Sci. 2014, 111, 14698-14703; (f) Milo, A.; Bess, E. N.; Sigman, M. S. Nature 2014, 507, 210-214; (g) Niemeyer, Z. L.; Milo, A.; Hickey, D. P.; Sigman, M. S. Nat. Chem. 2016, 8, 610-617; (h) Chen, Z. M.; Hilton, M. J.; Sigman, M. S. J. Am. Chem. Soc. 2016, 138, 1146111464; (i) Neel, A. J.; Milo, A.; Sigman, M. S.; Toste, F. D. J. Am. Chem. Soc. 2016, 138, 3863-3875; (j) Santiago, C. B.; Milo, A.; Sigman, M. S. J. Am. Chem. Soc. 2016, 138, 13424-13430; (k) Aguado-Ullate, S.; Urbano-Cuadrado, M.; Villalba, I.; Pires, E.; Garcia, J. I.; Bo, C.; Carbo, J. J. Chem. Eur. J. 2012, 18, 14026-14036; (l) Mansson, R. A.; Welsh, A. H.; Fey, N.; Orpen, A. G. J. Chem. Inf. Model. 2006, 46, 2591-2600; (m) Wu, K.; Doyle, A. G. Nat. Chem. 2017, advance online publication; (n) Piou, T.; RomanovMichailidis, F.; Romanova-Michaelides, M.; Jackson, K. E.; Semakul, N.; Taggart, T. D.; Newell, B. S.; Rithner, C. D.; Paton, R. S.; Rovis, T. J. Am. Chem. Soc. 2017, 139,

ACS Paragon Plus Environment

ACS Catalysis 1 2 3 4 5 6 7 8 9 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1296-1310; (o) Fey, N.; Orpen, A. G.; Harvey, J. N. Coord. Chem. Rev. 2009, 253, 704-722. (4) McCammant, M. S.; Sigman, M. S. Chem. Sci. 2015, 6, 1355-1361. (5) (a) Zhang, C.; Santiago, C. B.; Crawford, J. M.; Sigman, M. S. J. Am. Chem. Soc. 2015, 137, 15668-15671; (b) Harper, K. C.; Sigman, M. S. Proc. Natl. Acad. Sci. 2011, 108, 2179-2183. (6) Bess, E. N.; Guptill, D. M.; Davies, H. M. L.; Sigman, M. S. Chem. Sci. 2015, 6, 3057-3062. (7) (a) Yoo, K. S.; Park, C. P.; Yoon, C. H.; Sakaguchi, S.; O'Neill, J.; Jung, K. W. Org. Lett. 2007, 9, 3933-3935; (b) Delcamp, J. H.; Brucks, A. P.; White, M. C. J. Am. Chem. Soc. 2008, 130, 11270-11271; (c) Michel, B. W.; Camelio, A. M.; Cornell, C. N.; Sigman, M. S. J. Am. Chem. Soc. 2009, 131, 6076-6077; (d) Jiang, F.; Wu, Z. X.; Zhang, W. B. Tetrahedron Lett. 2010, 51, 5124-5126; (e) Schiffner, J. A.; Woste, T. H.; Oestreich, M. Eur. J. Org. Chem. 2010, 2010, 174-182; (f) McDonald, R. I.; White, P. B.; Weinstein, A. B.; Tam, C. P.; Stahl, S. S. Org. Lett. 2011, 13, 2830-2833; (g) Weinstein, A. B.; Stahl, S. S. Angew. Chem. Int. Ed. 2012, 51, 11505-11509; (h) De Crisci, A. G.; Chung, K.; Oliver, A. G.; Solis-Ibarra, D.; Waymouth, R. M. Organometallics 2013, 32, 2257-2266; (i) Mei, T. S.; Werner, E. W.; Burckle, A. J.; Sigman, M. S. J. Am. Chem. Soc. 2013, 135, 6830-6833; (j) Cao, Q.; Bailie, D. S.; Fu, R. Z.; Muldoon, M. J. Green Chem. 2015, 17, 2750-2757; (k) Race, N. J.; Schwalm, C. S.; Nakamuro, T.; Sigman, M. S. J. Am. Chem. Soc. 2016, 138, 15881-15884. (8) (a) Constant, S.; Tortoioli, S.; Müller, J.; Lacour, J. Angew. Chem. Int. Ed. 2007, 46, 2082-2085; (b) Linder, D.; Buron, F.; Constant, S.; Lacour, J. Eur. J. Org. Chem. 2008, 2008, 5778-5785; (c) Linder, D.; Austeri, M.; Lacour, J. Org. Biomol. Chem. 2009, 7, 4057-4061. (9) Werner, E. W.; Mei, T.-S.; Burckle, A. J.; Sigman, M. S. Science 2012, 338, 1455-1458. (10) Shimizu, H.; Holder, J. C.; Stoltz, B. M. Beilstein J. Org. Chem. 2013, 9, 1637-1642. (11) (a) Michel, B. W.; Steffens, L. D.; Sigman, M. S. J. Am. Chem. Soc. 2011, 133, 8317-8325; (b) Stokes, B. J.; Opra, S. M.; Sigman, M. S. J. Am. Chem. Soc. 2012, 134, 11408-11411. (12) (a) Werner, E. W.; Sigman, M. S. J. Am. Chem. Soc. 2010, 132, 13981-13983; (b) Zhang, C.; Santiago, C. B.; Kou, L.; Sigman, M. S. J. Am. Chem. Soc. 2015, 137, 7290-7293; (c) Patel, H. H.; Sigman, M. S. J. Am. Chem. Soc. 2015, 137, 3462-3465; (d) Zhang, C.; Tutkowski, B.; DeLuca, R. J.; Joyce, L. A.; Wiest, O.; Sigman, M. S. Chem. Sci. 2017, 8, 2277-2282. (13) (a) Hilton, M. J.; Xu, L. P.; Norrby, P. O.; Wu, Y. D.; Wiest, O.; Sigman, M. S. J. Org. Chem. 2014, 79, 1184111850; (b) Xu, L.; Hilton, M. J.; Zhang, X.; Norrby, P. O.; Wu, Y. D.; Sigman, M. S.; Wiest, O. J. Am. Chem. Soc. 2014, 136, 1960-1967. (14) (a) Burello, E.; Marion, P.; Galland, J.-C.; Chamard, A.; Rothenberg, G. Adv. Synth. Catal. 2005, 347, 803-810; (b) Fey, N.; Tsipis, A. C.; Harris, S. E.; Harvey, J. N.;

Page 8 of 10

Orpen, A. G.; Mansson, R. A. Chem. Eur. J. 2005, 12, 291302; (c) Hageman, J. A.; Westerhuis, J. A.; Frühauf, H.-W.; Rothenberg, G. Adv. Synth. Catal. 2006, 348, 361-369; (d) Fey, N. Dalton Trans. 2010, 39, 296–310. (15) (a) Stephens, P. J.; Devlin, F. J.; Chabalowski, C. F.; Frisch, M. J. J. Phys. Chem. 1994, 98, 11623-11627; (b) Vosko, S. H.; Wilk, L.; Nusair, M. Can. J. Phys. 1980, 58, 1200-1211; (c) Lee, C.; Yang, W.; Parr, R. G. Phys. Rev. B 1988, 37, 785-789; (d) Becke, A. D. J. Chem. Phys. 1993, 98, 5648-5652; (e) Zhao, Y.; Truhlar, D. G. Acc. Chem. Res. 2008, 41, 157-167. (16) Weigend, F.; Ahlrichs, R. Phys. Chem. Chem. Phys. 2005, 7, 3297-3305. (17) Grimme, S.; Antony, J.; Ehrlich, S.; Krieg, H. J. Chem. Phys. 2010, 132, 154104-154122. (18) (a) Hay, P. J.; Wadt, W. R. J. Chem. Phys. 1985, 82, 270-283; (b) Hay, P. J.; Wadt, W. R. J. Chem. Phys. 1985, 82, 299-310. (19) (a) Reed, A. E.; Curtiss, L. A.; Weinhold, F. Chem. Rev. 1988, 88, 899-926; (b) Weinhold, F.; Landis, C. R., Valency and Bonding: a Natural Bond Orbital DonorAcceptor Perspective. Cambridge University Press: 2005; (c) Glendening, E. D.; Landis, C. R.; Weinhold, F. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2012, 2, 1-42; (d) Weinhold, F. J. Comput. Chem. 2012, 33, 2363-2379; (e) Glendening, E. D.; Landis, C. R.; Weinhold, F. J. Comput. Chem. 2013, 34, 1429-1437. (20) Verloop, A., Drug Design Vol. III. Academic Press: 1976. (21) Freixa, Z.; van Leeuwen, P. W. N. M. Dalton Trans. 2003, 1890-1901. (22) (a) Hillier, A. C.; Sommer, W. J.; Yong, B. S.; Petersen, J. L.; Cavallo, L.; Nolan, S. P. Organometallics 2003, 22, 4322-4326; (b) Clavier, H.; Nolan, S. P. Chemical Commun. 2010, 46, 841-861. (23) (a) Fey, N.; Harvey, J. N.; Lloyd-Jones, G. C.; Murray, P.; Orpen, A. G.; Osborne, R.; Purdie, M. Organometallics 2008, 27, 1372-1383; (b) Dierkes, P.; van Leeuwen, P. W. N. M. J. Chem. Soc., Dalton Trans. 1999, 1519-1530. (24) Tropsha, A.; P., G.; Gombar, V. K. Mol. Inform. 2003, 22, 69-77. (25) Lee Rodgers, J.; Nicewander, W. A. Am. Stat. 1988, 42, 59-66. (26) (a) Topliss, J. G.; Edwards, R. P. J. Med. Chem. 1979, 22, 1238-1244; (b) Moore, B. C. IEEE Trans. Automat. Contr. 1981, 26, 17-32. (27) Golbraikh, A.; Tropsha, A. J. Mol. Graph. Model. 2002, 20, 269-276. (28) (a) Steyerberg, E. W.; Harrell Jr, F. E.; Borsboom, G. J. J. M.; Eijkemans, M. J. C.; Vergouwe, Y.; Habbema, J. D. F. J. Clin. Epidemiol. 2001, 54, 774-781; (b) Schüürmann, G.; Ebert, R. U.; Chen, J. W.; Wang, B.; Kühne, R. J. Chem. Inf. Model. 2008, 48, 2140–2145; (c) Consonni, V.; Ballabio, D.; Todeschini, R. J. Chemom. 2010, 24, 194-201. (29) Marquardt, D. W. J. Am. Stat. Assoc. 1980, 75, 87-91. (30) Grubbs, F. E. Ann. Math. Statist. 1950, 21, 27-58.

8 ACS Paragon Plus Environment

Page 9 of 10 1 2 3 4 5 6 7 8 9 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Catalysis

(31) Rorabacher, D. B. Anal. Chem. 1991, 63, 139-146. (32) Pickup, O. J. S.; Khazal, I.; Smith, E. J.; Whitwood, A. C.; Lynam, J. M.; Bolaky, K.; King, T. C.; Rawe, B. W.; Fey, N. Organometallics 2014, 33, 1751-1761.

9 ACS Paragon Plus Environment

ACS Catalysis 1 2 3 4 5 6 7 8 9 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 10

10 ACS Paragon Plus Environment