Commentary pubs.acs.org/accounts
A Holy Grail in Chemistry: Computational Catalyst Design: Feasible or Fiction? Published as part of the Accounts of Chemical Research special issue “Holy Grails in Chemistry”. Carl Poree and Franziska Schoenebeck* Institute of Organic Chemistry, RWTH Aachen University, Landoltweg 1, 52074 Aachen, Germany ABSTRACT: Efficient and selective catalysis lies at the heart of much of chemistry, enabling the synthesis of molecules and materials with enormous societal and technological impact. Modern in silico tools should allow us to develop new catalysts faster and better than ever before; this contribution discusses the feasibility and potential of computational catalyst design.
A
do we not routinely see catalysts emerging from computational design? How does it remain worthy of the “Holy Grail” epithet? Catalysis is a ubiquitous and integral component of modern chemistry. Homogeneous catalysis, particularly with transitionmetal-based catalysts, has transformed the ways in which chemists assemble molecules. However, while we have a number of predictive tools that we can apply to such systems, we are often presented with surprising reactivity that is not a priori predictable. If we can come to understand these reactivity modes, we can surely manipulate them to address challenging problems. Computational methods undoubtedly represent a means by which such understanding can be achieved; experience, however, teaches us that this is no easy task. The mechanisms of catalytic reactions are rarely trivial, often proceeding through short-lived intermediates at low concentrations, complicating any efforts to detect and characterize them. Indeed, there are cases where detectable intermediates represent catalytically inactive species or off-cycle reservoirs. Catalytic intermediates may interchange between various oxidation, coordination, charge, and even spin states and frequently carry ligands designed to steer the reactivity and selectivity of the metal center. The larger and more flexible these ligands are, the greater the conformational space that they may access along the reaction coordinates. The very nature of catalysis is to lower the energetic cost of a reaction, leading to a rather flatter potential energy surface than for a corresponding uncatalyzed process. These flatter surfaces are then more susceptible to perturbation by solvent or additives, which may have unexpectedly significant effects (Figure 1). Consequently,
core interest in chemistry is the creation of structure, and equally important today, function. The latter requires an understanding of the connection between structure and activity, ultimately allowing chemists to amend properties through rationally driven modifications. This approach is valued across disciplines, with applications in materials science, synthesis, catalysis, and biology. The drive to uncover the factors that dictate the properties and reactivity of molecules is at the core of physical organic chemistry, a field in which scientists for decades have made use of a range of experimental and theoretical tools to try and characterize molecular structure as well as rationalize reactivity. The one tool that does not involve the study of molecules in real time and therefore does not need physical matter as such, is computational chemistry. It enables the investigation of molecular behavior by simulation, the quantification thereof, and most interestingly, it potentially allows us to predict and design desired properties and reactivity. The computational tools available today are manifold, exploiting a range of physical models of varying degrees of sophistication and accuracy. Some are computationally cheaper and allow us to map the landscape in an approximate sense, while other (typically quantum chemical) methods allow us to much more accurately assess the most important parts of the energetic landscape, where bonds are made and broken. Owing to the impressive progress in theory, hardware, and software over the past few decades, many transformations can nowadays be computationally assessed on a time scale comparable to experiment. For example, while calculating the transition state for the Diels−Alder cycloaddition between butadiene and ethylene required about six months’ computational time in the 1980s, at what would today be considered a relatively modest (Hartree−Fock) level of theory, today the same calculation takes just a few seconds.1 Given these stunning advances, why © 2017 American Chemical Society
Received: December 4, 2016 Published: March 21, 2017 605
DOI: 10.1021/acs.accounts.6b00606 Acc. Chem. Res. 2017, 50, 605−608
Commentary
Accounts of Chemical Research
accurate calculation of energies, such as coupled-cluster methods (e.g., CCSD(T)), these “gold standard” methods are so computationally expensive that they cannot currently realistically be applied to the large number of possible structures we must consider in evaluating whole catalytic manifolds. Compromises are necessary at present; however there is no single method that is universally applicable, and it may not always be trivial (or even possible) to validate the results from less expensive methods by comparison with reference data; it is important to appreciate that even relatively small energy differences (on the order of 2 kcal/mol) can completely overturn the selectivity of a reaction. The large number of potential interactions between reactants, catalysts, and in situ generated intermediates would lead to an equally large number of calculations for the computational chemist to devise, set up, and analyze. If done in a manual manner, that is, in the traditional workflow outlined above, it will be a very time-consuming task. In this context, one may argue that alternative approaches may lead to a workable catalyst system more rapidly, for example, through automated high-throughput experimental screening, allowing hundreds of reactions to be carried out each day by a single chemist. However, such approaches rely upon the generation or acquisition of a library of catalysts. The potential for a biased sampling of chemical space remains, and difficult or unusual transformations may well require bespoke catalysts, which are not found in typical libraries. Such systems will most likely be identified through fundamental mechanistic insight. Fewer mechanistic possibilities need to be examined in systems for which existing data are available; more focused studies can be carried out in a comparatively short time frame. Meaningful advances have been made in this direction in the context of organocatalysis and transition-metal catalysis.3 An example was disclosed by our group in the context of aromatic trifluoromethylation. The investigation led to a ligand that was not previously investigated in catalysis nor part of any high-throughput screening library and was, on first sight, counterintuitive to existing reactivity data. The reductive elimination of Ar−CF3 from a palladium(II) center has been a problem of interest, which is possible with only a handful of ligands, typically wide-bite-angle ligands. However, computational investigations revealed that the bite angle of diphosphine ligands was not the crucial factor in determining the feasibility of reductive elimination but that the interaction of the phosphine substituents with the groups to be eliminated was of greater importance. Consequently, a new phosphine ligand bearing trifluoromethyl substituents was evaluated computationally (and subsequently experimentally verified): the hypothesized electrostatic repulsion of the palladium-bound CF3 group was found to be a significant contributing factor in destabilizing the palladium(II) complex, lowering the barrier to reductive elimination (Figure 3).4 However, the realization of de novo catalyst designs for completely unconstrained systems featuring numerous mechanistic possibilities necessitates the adoption of alternative approaches. An example is the judicious combination of design and screening (directed evolution). This approach has proven fruitful in the design of artificial enzymes, with a catalyst for the Kemp elimination having been developed, which accelerates the reaction by a factor of 6 × 108, for example.5 The starting point for further experimental evolution is obtained through initial computational assessment of the best transition state stabilization with a truncated model for a given transformation.
Figure 1. Challenges in computational catalyst design.
the question is arguably broader than “simply” one of catalyst design. For example, the neglect of unanticipated interactions with other reaction components, both the obvious and the less so, would all too frequently lead us to make false assumptions and waste time and resources on designing catalysts doomed to failure or discarding candidates with unexpected promise. Instead, the problem at hand should be more precisely framed as either one of designing a catalyst to fit within the constraints of an existing system or the even more challenging task of designing an entire catalyst system from the ground up. The level of complexity involved in identifying feasible mechanisms for a de novo designed catalyst in a given process is vast, involving the assessment of both productive and unproductive reactive pathways for the whole range of energetically accessible conformations (ligation states, spin states etc.). The latter is clearly an enormous task, and it is fair to ask whether such an assessment would ever be feasible (Figure 2). In evaluating potential reactivity modes, we must
Figure 2. Conceptual representation of reactivity evaluation using computational methods.
first find ground state and transition state geometries; the success of these calculations is highly dependent upon the input provided by the computational chemist and hence highly dependent on our intuition. With each set of calculations, we attempt to assess an individual reaction step. By implication, our investigation will only be as exhaustive as is our ability to ask salient questions. We are much less likely to find pathways that we have not previously conceived and intentionally sought to identify. Furthermore, the conventional approach of exploring a reaction’s potential energy surface in terms of stationary points alone as well as analysis in the context of transition state theory may not always be sufficient to explain and design reactivity and selectivity; consideration of dynamic effects may also be of importance.2 In this context, the accuracy of the computational approach is also of importance. While methods exist that allow for very 606
DOI: 10.1021/acs.accounts.6b00606 Acc. Chem. Res. 2017, 50, 605−608
Commentary
Accounts of Chemical Research
artificial intelligence. As we progress to understand how the human brain works, there will be advances in the creation of smart machines and technology. These might eventually be able to combine the hard-won knowledge gleaned from decades of research by countless chemists in a database with self-learning algorithms and path finders. Machines of sufficient “intelligence” (=learning from data analysis and the ability to make independent and smart decisions thereupon) and “knowledge” (richly fed database of storage capacity exceeding any human brain) may eventually even feature creativity and come up with smart ideas (=design) of their own.7 Returning to our heading question on whether truly de novo computational catalyst design will be feasible, it is safe to conclude that the design of catalysts requires us currently to be pragmatists, not purists. The most effective approach will be multipronged, harnessing the many advances that have been made in spectroscopy, synthesis, and experimental studies of reaction mechanism combined with computational chemistry. However, ever increasing technological advances will open radically different possibilities, and their implementation is expected to change also the way we approach and eventually solve this very question.
Figure 3. Computational evaluation of ligands allows Ph−CF3 reductive elimination from Pd(II) by reducing the activation barrier.
Following the search for the best fit to an enzyme scaffold, diversified catalyst libraries are subsequently generated by mutagenesis and directed evolution. Alternative computational approaches are found in ab initio molecular dynamics, in which the potential energy surface can be scanned in a less biased manner, principally allowing many possible pathways to be mapped under relatively realistic conditions (e.g., in explicit solvent). However, such methods are not yet free of challenges (theoretical and practical), and their application to complex catalytic systems with multiple reactive components, while exploring a range of candidate catalysts, remains to be seen. Several other developments are pursued in an attempt to eliminate or minimize user input and bias, for example, with evolutionary algorithms6a or fully automated and systematic searches, ultimately promising to identify favored reaction paths of complex reactions.6b,c In removing the biasing influence of the chemist’s preconceptions, these represent exciting departures from the traditional workflow outlined above. The development of userfriendly tools, suited for a synthetically focused community, would doubtless prove valuable. The computational design of catalytic systems is an exciting prospect, and one toward which we can, and doubtless will, make further strides. Our ability to predict and rationalize phenomena on the molecular level with computational methods continues to grow, and we will surely continue to exploit it to its fullest. Computational methods notwithstanding, the more intriguing question is one of design and what we mean by it. We can envisage two diametrically opposed approaches: do we truly seek to design, or can we facilitate an evolutionary process? Computationally driven evolution of catalysts is an exciting prospect because it would allow us to break free of the limitations of our ideas and preconceptions. However, one of the principal advantages of design is that we tend to confine ourselves to the synthetically tractable. Clearly, it would be all too easy for an unconstrained evolutionary process to devise catalysts we could never hope to make practical, and so perhaps we should consider a compromise. Directed evolution works because it deals with a constrained set of building blocks: amino acids. An evolutionary algorithm that is aware of available structural motifs, amenable to synthesis, must have potential. In addition to the many ongoing technical advances in theory, hardware, methods, automation, and algorithms, we expect a major shift forward in innovation and advance from
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Franziska Schoenebeck: 0000-0003-0047-0929 Notes
The authors declare no competing financial interest.
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REFERENCES
(1) Sperger, T.; Sanhueza, I. A.; Schoenebeck, F. Computation and Experiment: A Powerful Combination to Understand and Predict Reactivities. Acc. Chem. Res. 2016, 49, 1311−1319. (2) For a recent example, see Nieves-Quinones, Y.; Singleton, D. A. Dynamics and the Regiochemistry of Nitration of Toluene. J. Am. Chem. Soc. 2016, 138, 15167−15176. (3) For examples and reviews, see: (a) Houk, K. N.; Cheong, P. H.-Y. Computational prediction of small-molecule catalysts. Nature 2008, 455, 309−313. (b) Ianni, J. C.; Annamalai, V.; Phuan, P.-W.; Kozlowski, M. C.; Panda, M. A Priori Theoretical Prediction of Selectivity in Asymmetric Catalysis: Design of Chiral Catalysts by Using Quantum Molecular Interaction Fields. Angew. Chem., Int. Ed. 2006, 45, 5502−5505. (c) Tantillo, D. J. Using Theory and Experiment to Discover Catalysts for Electrocyclizations. Angew. Chem., Int. Ed. 2009, 48, 31−32. (d) Occhipinti, G.; Koudriavtsev, V.; Törnroos, K. W.; Jensen, V. R. Theory-assisted development of a robust and Z-selective olefin metathesis catalyst. Dalton Trans. 2014, 43, 11106−11117. (4) Nielsen, M. C.; Bonney, K. J.; Schoenebeck, F. Computational Ligand Design for the Reductive Elimination of ArCF3 from a Small Bite Angle PdII Complex: Remarkable Effect of a Perfluoroalkyl Phosphine. Angew. Chem., Int. Ed. 2014, 53, 5903−5906. (5) (a) Privett, H. K.; Kiss, G.; Lee, T. M.; Blomberg, R.; Chica, R. A.; Thomas, L. M.; Hilvert, D.; Houk, K. N.; Mayo, S. L. Iterative approach to computational enzyme design. Proc. Natl. Acad. Sci. U. S. A. 2012, 109, 3790−3795. (b) Jiang, L.; Althoff, E. A.; Clemente, F. R.; Doyle, L.; Rothlisberger, D.; Zanghellini, A.; Gallaher, J. L.; Betker, J. L.; Tanaka, F.; Barbas, C. F., III; Hilvert, D.; Houk, K. N.; Stoddard, B. L.; Baker, D. De Novo Computational Design of Retro-Aldol Enzymes. Science 2008, 319, 1387−1391. (6) (a) Foscato, M.; Houghton, B. J.; Occhipinti, G.; Deeth, R. J.; Jensen, V. R. Ring Closure To Form Metal Chelates in 3D Fragment607
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Commentary
Accounts of Chemical Research Based de Novo Design. J. Chem. Inf. Model. 2015, 55, 1844−1856. (b) Wang, L.-P.; Titov, A.; McGibbon, R.; Liu, F.; Pande, V. S.; Martínez, T. J. Discovering chemistry with an ab initio nanoreactor. Nat. Chem. 2014, 6, 1044−1048. (c) Sameera, W. M. C.; Maeda, S.; Morokuma, K. Computational Catalysis Using the Artificial Force Induced Reaction Method. Acc. Chem. Res. 2016, 49, 763−773. (7) For a recent review, see: Szymkuć, S.; Gajewska, E. P.; Klucznik, T.; Molga, K.; Dittwald, P.; Startek, M.; Bajczyk, M.; Grzybowski, B. A. Computer-Assisted Synthetic Planning: The End of the Beginning. Angew. Chem., Int. Ed. 2016, 55, 5904−5937.
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