Small-Molecule Systems Chemistry

Review

Small-Molecule Systems Chemistry  Miljanic1,* Ognjen S.

Systems chemistry is a broad and relatively new approach to studying chemical phenomena by embracing and understanding their inherent complexity rather than seeking to limit or eliminate it. Systems-level understanding is a powerful alternative to the traditionally reductionist approach that many branches of chemistry currently utilize. Complex chemical systems can operate under kinetic or thermodynamic control and give rise to unique properties, some of which are emergent—that is, impossible to observe in individual components of the system. This review highlights selected examples of how the systems chemistry approach has been used for studying dynamic combinatorial libraries, autocatalytic processes, chemical reactions at the origins of life, as well as for reaction discovery and synthesis of complex functional molecules, including molecular machines.

INTRODUCTION Webster’s Third New International Dictionary defines a system as ‘‘a complex unity formed of many often diverse parts subject to a common plan or serving a common purpose.’’1 According to this definition, humans are surrounded by and constitute an integral part of numerous systems: examples of such complex unities include society, climate, or natural ecosystems but also stock markets and web-based social networks. Going down the length scale, complex systems are also found in metabolic pathways and neuronal networks. A unifying feature of these diverse systems is that the fates of individual actors in such networks are determined not just by the independent merits of the actor itself but also by the system as a whole. For example, the value of a small company on a stock exchange can be dramatically influenced by the swings in the value of a much larger ‘‘market mover,’’ even if the two operate in entirely unrelated sectors of economy. In addition, complex systems can give rise to new, unexpected, and often useful emergent properties, which are characteristic only of the system as a whole and cannot be traced back to an individual component. Examples of macroscopic emergent behaviors include global warming and climate change, trending phenomena on social networks, stock market crashes, or feedback loops in predator populations. A number of disciplines study complex systems, with a varying degree of appreciation of this inherent complexity. Some fields—such as economics, urban planning, ecology, or systems biology—have embraced the complexity as a necessary feature in the analysis of their topics of study. In chemistry, the situation is divided. Certain branches of chemistry, such as ocean and atmospheric chemistry, have viewed their respective objects of study as interconnected reaction networks from their very inception as research areas. The same can be said of biochemistry of metabolic pathways. Modern analytical chemistry cannot afford to ignore the interactions among the components of target mixtures. On the other hand, some branches of physical and (especially) synthetic chemistry have opted for a reductionist approach to studying compounds in isolation, effectively ignoring many aspects of their inherent complexity. Synthetic chemistry is rather unique among the sciences in that it creates its own subject of study and is

502

Chem 2, 502–524, April 13, 2017 ª 2017 Elsevier Inc.

The Bigger Picture Some of the key scientific advances of the past century— nuclear energy, artificial fertilizers, or vaccines—came from the domain of a single discipline: physics, chemistry, or biology. As the 21st century unfolds, it is increasingly clear that the pressing problems in energy, climate, and medicine will not be solved in the same way. Instead, they will need close cooperation of the traditional branches of science. Such collaborative ventures will necessitate understanding of complex systems in their entirety and will build on the reductionist understanding of a system’s individual components. Key insights from this systems-level approach will be related to the interactions between actors in a complex unity, emergent properties observable only within a system rather than assigned to individual actors, and the phenomena that occur across different length scales. Systems chemistry aims to retool the traditional understanding of chemical phenomena to be better suited to these global challenges.

thus able to keep things simple by eliminating undesirable side effects of complexity. This approach works remarkably well: as Whitesides pointed out in his 1999 perspective,2 a typical chemical reaction involves 1022 molecules, each with dozens of potentially reactive bonds. Nevertheless, these vast collections of molecules for the most part react as one. Examples of this reductionist approach abound and are found in many unifying concepts of synthetic chemistry, such as the notion of functional groups, which assumes—mostly correctly—that any molecule with the same reactive fragment will behave in the same way under given reaction conditions. Systems chemistry is often defined by analogy with other systems-oriented disciplines (especially systems biology) as the field of chemistry that studies the behavior of complex chemical systems, with a particular interest in the unprecedented properties unique to these complex entities. The systems approach to the traditionally reductionist subdisciplines of chemistry appears to be gaining traction. In my view, several reasons are enabling and necessitating this shift: (1) modern analytical and computational techniques can now tackle increasingly complex subjects—and are often specifically developed for the study of multicomponent systems; (2) the systems approach is often indispensable with the increasingly interdisciplinary nature of chemical enterprise; and (3) increased utilization of complex mixtures of biological origin often requires more comprehensive understanding of their interactions. Dedicated use of the term ‘‘systems chemistry’’ is relatively recent, having first been introduced by von Kiedrowski3 and subsequently elaborated by Otto in three excellent reviews.4–6 Systems chemistry was also a subject of a web-themed issue of Chemical Communications in 2014,7 and the specialized (but short-lived) Journal of Systems Chemistry published 36 articles before being discontinued in 2015.8 Many intriguing behaviors can be observed in complex chemical systems with multiple components. These can be divided into the following:
Behaviors directly associated with individual components, which are simply observed in parallel within a large system.
Behaviors that can be observed only in the complex system but can be predicted through detailed understanding of the properties of individual components.
Behaviors that can be observed only in the complex system and cannot be predicted, no matter how thorough our understanding of individual components is. The last two classes of behaviors are referred to as systems-level behaviors, and the behaviors in the last class are truly emergent phenomena. It should be noted that the term ‘‘emergent phenomenon’’ is probably overused today, commonly to signify any systems-level behavior. Whether a phenomenon is truly unpredictable—or only unpredictable because of our limited understanding—can be viewed as a very subjective assessment. However, Darley9 has shown that there indeed are truly emergent phenomena and has rigorously defined them as those ‘‘for which the optimal means of prediction is simulation,’’ after which he laid down the mathematical framework for emergence. This is not to say that only emergent phenomena are worthwhile for study; in fact, the more-or-less predictable phenomena from the first two classes can be extremely useful, and furthermore, their exploitation within a systems context (rather than in isolation) can be beneficial. In addition, the behaviors described above can be classified into thermodynamically and kinetically controlled ones. The former are observed at equilibrium and are thus rather unlikely to be emergent because the position of equilibrium can be predicted with sufficiently detailed understanding of the components of the system. The latter involve situations where the system is either kinetically trapped in a local thermodynamic minimum or found

1Department of Chemistry,

University of Houston, Houston, TX 77204-5003, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.chempr.2017.03.002

Chem 2, 502–524, April 13, 2017

503

in a far-from-equilibrium state whose maintenance requires continuous input of fuel. Most of the discussion in this review will focus on the behaviors and reactions proceeding under kinetic control. Systems chemistry appears to be more of a conceptual approach that can be applied to any chemical problem rather than a subdiscipline with its own well-defined set of problems. Thus, this review will not strive for comprehensiveness, because I doubt that would be possible or particularly useful. Instead, it will review significant and recent advances in systems-level understanding of (1) dynamic combinatorial libraries, (2) autocatalysis and oscillatory reactions, and (3) chemical reactivity at the origins of life, and then it will proceed to highlight the nascent efforts to introduce systems-level analysis into areas that have traditionally not used it: (4) reaction discovery and (5) synthesis and functional utilization of complex molecules. In fact, the scientists working in the last two areas almost never describe their work under the term ‘‘systems chemistry,’’ highlighting some of the difficulty and controversy in defining the field. In all of these subsections, I will direct the reader to the related reviews and perspectives on systems chemistry and specialized aspects of it before proceeding to offer personal, subjective, and incomplete selection of recent illustrations of systems chemistry principles in these contexts.

DYNAMIC COMBINATORIAL CHEMISTRY Dynamic combinatorial chemistry (DCC)10,11 studies mixtures of compounds formed through reversible (dynamic) reactions and the response of those mixtures to external stimuli. The relationship between DCC and systems chemistry has thus been a logical fit.12 In the thermodynamically controlled DCC world, energy surfaces connecting the different local minima are shallow and easily traversed, and DCC has presented a complementary strategy to the classic arsenal of synthetic chemistry, consisting mostly of the reactions proceeding under kinetic control. The reversibility inherent to dynamic chemistry allows for error correction of the undesirably formed bonds as the system approaches the thermodynamic minimum. This ability to correct errors enables synthesis of highly complex structures in virtually quantitative yields because side products are never lost to the course of reaction—they’re just temporarily sequestered away. DCC has been used to quickly produce structures of admirable complexity; Stoddart’s Borromean rings13 are just one of many examples of beautiful symmetric structures produced with DCC. More recently, Schmittel and coworkers carefully designed experiments in which a thorough understanding of the stoichiometry and binding geometry of multiple reaction partners resulted in the exclusive production of sophisticated low-symmetry structures from a dynamic mixture.14–16 Furthermore, because dynamic combinatorial libraries (DCLs) can respond to external stimuli through the Le Chaˆtelier principle, they can amplify those library members that best adapt to the disturbing stimulus. This feature allows the transformation of complex ‘‘messy’’ libraries under the influence of an external guest into a single compound that acts as a superior receptor for that guest. Such a strategy was used in the generation of receptors for biomolecules17 and fullerenes,18 as well as the creation of catalysts for specific reactions through the recognition of transition state analogs for these reactions.19,20 All of these behaviors can be classified as belonging to the second class discussed above. For their identification, the systems approach was necessary, but all of them could have been predicted with sufficient insight into the properties of individual DCL members. Famously, Borromean rings were computationally predicted by the Stoddart group significantly before their synthesis was realized. Despite this apparent predictability, the DCC approach to these self-assembled structures is still very valuable because it both (1) allows

504

Chem 2, 502–524, April 13, 2017

Scheme 1. Sometimes DCL Distributions Are Not Obvious An equimolar mixture of randomly reacting precursors (A–C) can form ten different assemblies. If all of those assemblies are equally stable, ABC, with 22.2% relative concentration (versus 3.7% for homomeric A3, B3, and C3), will be the most likely component of the DCL. An even more interesting case arises if A3 and ABC are both stabilized (e.g., by guest binding) 1,000-fold in relation to other DCL members; this stabilization lowers the relative concentration of A3 to just 0.4%, leaving ABC as the dominant product (98.6%), because the latter better represents the composition of the overall DCL.

parallel—rather than serial—screening of potential self-assembled catalysts or receptors and (2) dramatically simplifies the synthesis of such complex structures. DCC is also often used to discover the thermodynamically most stable state of the system in the absence of a detailed theoretical prediction (given that many experimentalists guide themselves by the ‘‘Why calculate when you can measure?’’ maxim) and has therefore been a great tool for (3) identification of unexpected structures through apparent serendipity. In larger DCLs, predicting the equilibrium state is still possible but often leads to non-obvious conclusions. Grote et al.21 have shown that, in DCLs composed of multiple components, relative thermodynamic stability of the resultant assemblies is not the only factor that determines their relative populations at the end of assembly. In such multicomponent DCLs, heteromeric members that better reflect the stoichiometric composition of the library are found to be favored. In an example shown in Scheme 1, combination of equimolar amounts of three building blocks (A, B, and C) into a cyclic trimer can result in a total of ten assemblies; three are homomeric, and the other seven combine two or three different building blocks. If all of these assemblies are equally stable, homomeric A3 (as well as B3 and C3, not shown) will constitute 3.7% of the total library material, whereas heteromeric ABC—which better represents the composition of the overall library—will be present in 22.2%. Even

Chem 2, 502–524, April 13, 2017

505

more intriguingly, if both a heteromeric (ABC) and one of the homomeric (A3) assemblies are stabilized 1,000-fold in relation to the other components of the library, this stabilization in fact lowers the concentration of A3 to just 0.4%! Heteromeric ABC dominates in such a DCL with 98.6% relative concentration. These calculations were confirmed experimentally with a series of metal complexes whose stability could be modulated with increasing steric bulk of the ligands. A number of other not immediately obvious conclusions have been demonstrated even in DCLs composed of a single component: smaller assemblies tended to be favored in the presence of a target species, even if the target binding constant was the same for both the small and large assemblies.22 Corbett et al.23 have subsequently extended this study to significantly larger DCLs and reached a similar conclusion: that amplification of the strongest binder for a given template might not be guaranteed in DCLs where other moderately strong, but smaller, competitors might be present. This effect was particularly pronounced at high concentrations of templates. Ludlow et al.24 have also reported a method that allows the determination of binding affinities in DCLs directly from library product distributions—that is, without isolation of individual complexes—via numeric analysis that can be automated with their DCLFit program. Recently, there has been increased emphasis on the study of kinetically controlled behaviors of DCLs because these can potentially yield even more intriguing results that cannot be easily predicted through the understanding of individual components.25 In these protocols, the final state of the system is not just the function of its overall stability but also the function of the (reaction) pathway taken to get to that state. The reversibility of the underlying DCLs plays a role in allowing the communication between the members by transferring information—as well as material—in response to external stimuli. In such a scenario, a highly selective kinetically controlled stimulus can be used to dramatically amplify or even invert the preferences exhibited at equilibrium. In the field of dynamic combinatorial resolution, Ramstro¨m and coworkers26 have shown that a dynamic mixture of nitroaldols can be resolved during the course of enzyme-mediated acetylation. Starting with an equimolar mixture of aldehydes (1–5; Scheme 2), a nitroaldol reaction with 6 produces a pool of ten possible nitroaldol adducts, present as five pairs of enantiomers. Upon exposure of this mixture to Burkholderia cepacia lipase PS-C I and an acetyl transfer agent, just two acetylated products are observed: (R)-7 and (R)-8. Both are isolated in high enantiomeric excess from a pool of ten nitroaldol adducts. In a subsequent extension of this protocol, double kinetic resolution has been demonstrated during the course of imine cyanation (Strecker reaction).27 Here, the enzyme Burkholderia cepacia lipase PS-C I was able to select three Strecker adducts with a clear preference for the smallest amine and electron-poor aldehyde components from a pool of 24 potential substrates. The reversible nature of this cyanation allowed the exchange of material between the precursors and subsequent amplification of the most preferred substrates. My group has been active in studying the phenomena of kinetically controlled selfsorting28–30 of imine- and ester-based DCLs. These libraries were constructed by random condensation of n aldehydes with n amines (for imine libraries) or n alcohols with n carboxylic acids (for ester libraries). The resulting DCLs thus had n2 members. An irreversible external stimulus was then applied to the DCL, and it preferentially operated on the imine or ester component that best responded to it (the most volatile component in the case of distillation and the most electron-rich in the case of oxidation). The consumption of that fast-reacting DCL member resulted in the rearrangement of the mixture so that more of the just-consumed member was generated

506

Chem 2, 502–524, April 13, 2017

Scheme 2. Dynamic Combinatorial Resolution of a DCL A DCL formed by a nitroaldol reaction between a mixture of 1–5 and 6 can be resolved during the course of enzyme-mediated acetylation into just two final products, (R)-7 and (R)-8. Both are isolated with high enantiomeric excess.

from all other DCL components that shared either an aldehyde or a ketone subunit with it. Under the continued application of the irreversible stimulus, these mixtures would lose not just their most reactive member but also a total of 2n  1 members in the process, greatly simplifying in composition. Iterative application of this protocol

Chem 2, 502–524, April 13, 2017

507

Scheme 3. Self-Sorting of a Dynamic Imine Library during Chromatography A DCL constructed from imines 9–17 simplifies in composition during chromatographic purification to yield only imines 9, 13, and 17, which are positioned along the ‘‘diagonal of polarity.’’

would reduce the complexity of the mixture from n2 to just n components that can be produced in high yields and high purities. Diagonal isolation of DCL members during the course of self-sorting is certainly not unpredictable, but it is arguably still useful because it permits the production of multiple pure compounds in a single reactor. We have demonstrated applications of this principle during the oxidation,31 distillation,32 precipitation,33 and chromatographic34 separation of imine and ester35 DCLs. The use of phase separation was reported to achieve analogous sorting of imine species36 and coordination cages.37,38 As an example, self-sorting of a dynamic imine library during chromatographic purification is shown in Scheme 3. In this procedure, an equimolar mixture of imines (9–17) is subjected to column chromatography on silica. Fast-eluting nonpolar 9 is removed from the column first; its depletion in the DCL causes re-equilibration of 10–17, which produces more 9 at the expense of 10–12 and 15, all other imines that share a constituent with 9. Subsequent repetition of this process isolates 13 and finally the least polar imine 17. Schaufelberger and Ramstro¨m have used kinetic self-sorting as a strategy to identify dynamic covalent catalysts for the Morita-Baylis-Hillman (MBH) reaction.39 The fascinating phenomenon of molecular self-replication40—wherein a molecule generates an exact copy of itself—has been explored in the context of DCC by del Amo and Philp.41 Within a DCL that is at equilibrium, the effects of self-replication are subtle because autocatalytic production of one library member does not change its stability—only the rate at which it is formed.42 Thus, the amount of the self-replicating molecule that can be produced remains fundamentally limited. However, switching to a library in which a single (non-self-replicating) component irreversibly reacts to form a self-replicating product can overcome this thermodynamic

508

Chem 2, 502–524, April 13, 2017

limitation. Philip and colleagues have demonstrated this behavior in a small DCL that combines imine and nitrones (Scheme 4).43,44 Starting with 18–21, dehydration generated a random dynamic mixture of 22–25 (top panel). Exposure of this DCL to maleimides 26a and 26b (in two separate experiments) resulted in a 1,3-dipolar cycloaddition of 26a and 26b with both available nitrones 23 and 24; imines were not reactive in this reaction (central panel). When maleimide 26a was used, a mixture of four adducts was obtained: compounds 27 (from reaction with 24) and 28 (from reaction with 23), both observed as cis- and trans-adducts, but without significant selectivity. Switching to maleimide 26b, which had a free –COOH group available for hydrogen bonding, resulted in the production of the adduct trans-28b, which could template its own formation through ternary complex 23$26b$trans-28b (bottom panel). This templation accelerated the production of trans-28b 100-fold, and this self-replicating adduct quickly became the dominant species in the product pool (80% after 16 hr).

AUTOCATALYSIS AND OSCILLATORY REACTIONS Self-replicating behaviors within DCLs are a subset of autocatalytic reactions45 in which a template can find its own building blocks in the reaction mixture and catalyze their reaction to produce more of itself. Autocatalytic behaviors are themselves a subset of a larger set of nonlinear chemical dynamic phenomena,46 which also include positive- and negative-feedback loops.47 Such nonlinear kinetics can give rise to emergent behaviors such as multistability, periodic oscillations, or chaos. When nonlinear reactions proceed in structured media such as microemulsions, droplet arrays, or hydrogels, they can further result in the creation of rich spatiotemporal patterns that can serve as advanced but still tractable models of biological behaviors.48 At present, the pool of available small-molecule organic autocatalytic reactions is severely limited.49,50 Certainly the best studied is Soai’s asymmetric addition of diethylzinc onto pyrimidyl aldehyde (31; Scheme 5, top).51 When this reaction is conducted in the presence of even small excess amounts of chiral alcohol (S)-29, it yields chiral alkoxide (S)-30, which acts as an autocatalyst; its hydrolysis during the workup generates more (S)-29. Even in the parent system (R = H), which suffers from relatively moderate yields, increases in the initial amount of (S)-29 are greater than 1,000-fold. Soai et al.52 have found that precursors with 2-alkynyl substitution act as superb autocatalysts that replicate high enantiomeric excesses in virtually quantitative yields. On the other hand, 2-methyl substituted derivatives can dramatically amplify miniscule enantiomeric excesses caused by physical stimuli (including photolysis by circularly polarized light). Soai’s group has further demonstrated that miniscule desymmetrization of molecules—caused by differences between 12C and 13C isotopes of carbon—can be amplified by this autocatalytic procedure.53 These features appear to provide a viable and extremely important experimental link between origins of chirality and autocatalysis. In 2007, Tsogoeva and coworkers54 disclosed the first report of an asymmetric autocatalytic reaction proceeding without a metal species (i.e., asymmetric organoautocatalysis). A Mannich reaction between acetone and precursor 32 (Scheme 5, bottom) produced adduct 33. If this adduct was independently prepared in an enantiopure form and then used as the catalyst in the said Mannich reaction, the enantiomeric excess and chirality sense of the final product closely followed that of the added catalyst, suggesting an autocatalytic behavior. This suspicion was confirmed by rigorous control experiments. Subsequently, the same team demonstrated that an autocatalytic aldol reaction between p-nitrobenzaldehyde and acetone can result in spontaneous mirror-symmetry breaking.55,56

Chem 2, 502–524, April 13, 2017

509

Scheme 4. Self-Replication in a DCL In a four-component DCL (22–25), only nitrones react with maleimides 26a and 26b. If maleimide 26b (capable of hydrogen bonding) is used, adduct trans-28b will be capable of self-replicating through ternary complex 23$26b$trans-28b.

510

Chem 2, 502–524, April 13, 2017

Scheme 5. Examples of Autocatalytic Reactions Only a handful of small-molecule autocatalytic reactions are known in organic chemistry. Top: Soai’s asymmetric addition of diethylzinc onto pyrimidine-based aldehyde 31 generates alkoxide (S)-30, which catalyzes its own formation. Seeding the mixture with a small amount of (S)-enriched alcohol 29 can result in an approximately 1,000-fold increase in the amount of (S)-29. Bottom: Tsogoeva’s observation of asymmetric organoautocatalysis in the synthesis of Mannich adduct 33.

Very recently, Whitesides and coworkers reported a novel autocatalytic all-organic reaction that is subject to exquisite synthetic control.57 In an initial mixture containing alanine thioester (34) and cystamine (35) (Scheme 6), thiol-thioester exchange was not possible because there were no free thiols. These were generated by slow hydrolysis of 34, which released free alanine and ethanethiol (36), which acted as the trigger for subsequent autocatalysis. Reaction of 36 and 35 produced disulfide 37 as well as cysteamine (38). Reaction of 34 and 38 generated two thiols: another molecule of ethanethiol and 39 (which was formed through an intramolecular Kent ligation). Both of these thiols reacted with 35, releasing disulfides 37 and 40, as well as one molecule of 38 each; overall, one molecule of 38 that entered this reaction resulted in the production of two copies of itself, constituting an autocatalytic species. Sequestering thiols 36 and 39 through a thiol-ene reaction with maleimide 41 can also inhibit autocatalysis. This autocatalytic network of simple reactions can be easily modified through substitution, allowing the adjustment of individual rates; further, it involves molecules that have biological and potentially prebiotic relevance, suggesting a potential pathway for autocatalysis in the absence of enzymes. By adjusting the concentrations of reacting species and using a continuously stirred tank reactor, the Whitesides group demonstrated that this system can exhibit bistability and well-behaved oscillations in concentrations of the thiol species.57 In his seminal (and very approachable) 1952 paper ‘‘The Chemical Basis of Morphogenesis,’’58 Alan Turing presented a mathematical framework for the disruption of the initial chemical homogeneity of samples. His perspective showed how miniscule oscillations in the concentrations of reactive species can be amplified through differences in the rates of both chemical reactions and component diffusion. Such critical emphasis on kinetics then allows the creation of nonhomogeneous spatiotemporal patterns, some of

Chem 2, 502–524, April 13, 2017

511

Scheme 6. Organic Autocatalytic Reaction Subject to Inhibition and Oscillatory Behavior Whitesides and coworkers have shown autocatalytic behavior in a relatively simple reaction network consisting of species 34–41. Slow hydrolysis of 34 releases free thiol 36, which then generates 38 from disulfide 35, which was present at the beginning of the reaction. Compound 38 then duplicates because its reaction with 34 creates two equivalents of thiols (one each of 36 and 39), both of which create more 38 from 35. Sequestering free thiols in a maleimide-thiol adduct with 41 can effectively inhibit autocatalysis.

which have eventual relevance to biology. In purely chemical systems, combinations of autocatalytic and other processes with nonlinear kinetics can result in oscillatory behaviors and pattern formation. The field of nonlinear chemical dynamics was initiated by the well-known Belousov-Zhabotinski reaction(s),59 which have been explored profusely in the context of systems chemistry. Although well studied, the recent advent of flow and gel reactors allowed identification of previously unknown phenomena, including Turing patterns,60 in this prototypical oscillatory reaction. In 2016, Philip’s group61 reported the use of their nitrone-maleimide self-replicating system (described in Scheme 4) to drive a propagating reaction-diffusion front. In a clever experimental design, fluorine substituent of 23 was replaced with a (9-antracenyl)ethynyl group that served as a fluorescent optical tag to follow the progress of self-replication.

512

Chem 2, 502–524, April 13, 2017

Then, a mixture of this derivative and 26b was placed into two identical gas-tight microsyringes (to prevent the evaporation of solvent, which would shut down the reaction). In one, it was left to react on its own, whereas in the other, the autocatalytic cycloadduct of the two starting materials was added. Time-lapse photographs of fluorescence emission showed clear propagation of the reaction front starting from the added seed of autocatalyst. This was the first demonstration of propagating reaction fronts in a purely organic reaction.

STUDIES OF THE ORIGIN OF LIFE Systems chemistry is also of key importance in studies of chemistry at the origins of life (see Islam and Powner62 in this issue of Chem). In a 2014 review, de la Escosura and coworkers63 posited that the traditional dichotomy of metabolism-first and gene-first theories of the origin of life can be quite misleading because both theories appear to be too ‘‘kinetically fragile’’ in that they easily revert to a thermodynamic minimum. The two can be reconciled by a systems-level approach that treats the problem as a system in which both metabolism and information transfer evolved roughly simultaneously, along with the evolution of cellular boundaries that prevented dilution of this reactive far-from-equilibrium content. Another point of debate in the field is whether studies of the origin of life should seek to replicate extant biochemistry (e.g., in alkaline hydrothermal vents)64 or to create the molecules of life by using more traditional synthetic chemistry. Systems-level understanding and autocatalysis could have played a role in the formation of simple sugars through the formose reaction (Scheme 7), which was originally discovered by Butlerov in 186165 and thoroughly mechanistically investigated by Breslow.66 In this autocatalytic process, formaldehyde (42, which could have plausibly formed on prebiotic Earth) initially slowly dimerizes into glycolaldehyde (43), and then, through a series of tautomerizations and aldol additions, forms higher sugars, including tetroses 46 and 47, and pentoses 48 and 49. One of the formed tetroses, 47, can further fragment into two molecules of glycolaldehyde, completing an autocatalytic cycle. The formose reaction is notoriously unselective in the absence of additives, and some of its products are unstable upon prolonged exposure to the formose reaction conditions. However, the addition of borate minerals has been shown to selectively stabilize ribose products,67 whereas silicates appear to favor four- and six-carbon sugars.68 The viability of ribose and other sugars under prebiotic conditions is still a long way from providing a blueprint for the prebiotic synthesis of DNA and even nucleotides, given the difficulties in the coupling of these primitive sugars with weakly nucleophilic nucleobases. Sutherland and coworkers69 proposed a prebiotically viable synthesis of ribonucleotides that circumvents their most obvious assembly from a ribose sugar and a nucleotide, thus bypassing the traditionally assumed separation of carbon-oxygen (ribose) and carbon-nitrogen (nucleobase) chemistry. Instead, starting with 43 and cyanamide (Scheme 8), oxazole 51 can be produced in the presence of an inorganic phosphate as the catalyst. Its condensation with 44 proceeds to give 52, which can further react with cyanoacetylene (53) in the presence of a phosphate buffer to produce arabinose anhydronucleoside (54). Final phosphorylation rearrangement of 54 results in the activated b-ribocytidine-20 ,30 -cyclic phosphate 55. It was also demonstrated that ultraviolet radiation proceeds to destroy most of the side products—thus avoiding the caveat of the ‘‘chemical combinatorial explosion’’70—while also converting 55 into b-ribouridine-20 ,30 -cyclic phosphate (56, the other pyrimidine component of RNA). In this complex chemical system, phosphate does not get incorporated into the structure of the

Chem 2, 502–524, April 13, 2017

513

Scheme 7. Formose Reaction: An Autocatalytic Process that Creates Simple Sugars In a formose (a combination of ‘‘formaldehyde’’ and ‘‘aldose’’) reaction, the initial condensation of formaldehyde (42) with itself results in glycolaldehyde (43). Through a series of tautomerizations and formaldehyde additions, tetrose 47 is generated, which can fall apart into two molecules of 43, completing an autocatalytic cycle that sees one molecule of 43 transform into two with formaldehyde as the chemical fuel. Apart from tetroses, this process can generate pentoses such as 48 and 49, which can be stabilized by coordination to borate minerals.

final product until the last step, but its presence is nevertheless essential from the beginning because it plays the roles of catalyst and buffer. The same group also reported that a multicomponent reaction can simultaneously account for the activation of nucleotides (thought to be requisite for their polymerization into RNA) and the synthesis of amino acid amides. Namely, starting from, e.g., 30 -nucleotide 57 (Scheme 9), reaction with ammonium chloride, t-butylisocyanide (58), and isobutyaldehyde (59) with overnight heating resulted in a heterogeneous mixture. Its fractionation revealed the presence of 60 (a cyclized derivative of 57), as well as hydroxyl amide 61, amino amide 62, and hydroxyl amidine 63; 61–63 were all derived from valine.71 Sutherland’s group72 and others73 noted that their simulations of reaction networks proposed in the origins of biomolecules operated under carefully controlled conditions

514

Chem 2, 502–524, April 13, 2017

Scheme 8. Synthesis of a Ribonucleotides under Plausible Prebiotic Conditions Starting with 43 and 50, oxazole 51 can be formed in the presence of phosphate catalyst. Addition of 44 over its C=C bond results in 52, which reacts with cyanoacetylene (53) to produce 54. Phosphorylation yields 55, which can be photolytically hydrolyzed into 56, sidestepping the problematic coupling of ribose to nucleobases in the formation of ribonucleotides.

of the synthetic laboratory rather than under crude one-pot prebiotic conditions. They hypothesized that such a one-pot scenario might not be the only way that the molecules of life could have formed; instead, a reaction network could have developed over space (in separate streams and pools) and time (over dissolution and reprecipitation cycles) in a prebiotic analog of a flow reactor. A nonenzymatic version of some of the existing biochemical reaction networks (that are today enzyme catalyzed) could have played a role in the origins of life. For example, it has been shown that parts of the tricarboxylic acid (TCA, or Krebs) cycle can operate in reverse—i.e., in the anabolic direction—without enzymes by using ZnS as the mineral photocatalyst.74 This reductive TCA (rTCA) cycle is autocatalytic, and its components are the key intermediary metabolites: lipids, sugars, and amino acids can all be derived from members of the rTCA cycle.75

REACTION DISCOVERY Development of new reactions and their catalytic versions has been the focus of much scientific effort in the past several decades. This has also been one of the key areas of chemical enterprise focusing on the reductionist approach. In a typical reaction development effort, numerous reaction conditions are serially screened before an optimal combination of metals, ligands, solvents, and other additives is identified. Then, such an optimized catalytic system is exposed to a variety of substrates in an effort to determine the scope and limitations of the newly discovered reaction. Although this approach continues to yield impressive results, recent years have seen significant efforts devoted to making this enterprise more parallel by using high-throughput screening techniques and robotic systems.76,77 In these approaches, one can either (1) assess the reactivity of numerous substrates in parallel and deconvolute new reactivity from the results or (2) use a library of multiple potential catalysts on a single substrate and then deconvolute the catalyst truly responsible for the observed reactivity. In a simple yet powerful demonstration of the first

Chem 2, 502–524, April 13, 2017

515

Scheme 9. Simultaneous Activation of Nucleoside Phosphates toward Polymerization and Synthesis of Amino Acid Amides Using isocyanides and aldehyde precursors in the presence of nucleoside phosphate monoester 57 yielded cyclized nucleoside phosphate 60, along with amino acid derivatives 61–63. Compound 60 is activated in relation to 57 and considered to be important in the polymerization into RNA.

approach, Robbins and Hartwig78 screened a pool of 17 substrates (64–80), chosen so that any coupling reaction should give a product with a mass distinct from all starting materials (Scheme 10). This mixture was placed into each well of a 384-well plate; then, each row on the plate was treated with a single Earth-abundant metal source, and each column was treated with an organic ligand. After heating, the mixtures were analyzed by mass spectrometry. The presence of a high-molecular-weight product in a given plate meant that a coupling reaction occurred. The row and column in which the reactive well was found allowed quick identification of the metal and ligand responsible for the observed coupling, and the product’s mass suggested the identity of the starting materials involved in its formation. This suggestion was then confirmed through sequential narrowing of the pool of 17 reactants until the two coupling partners were identified. This method enabled the expedient discovery of several new reactions, including Ni-catalyzed hydroarylation of alkynes with boronic acids (Scheme 10, bottom left) and Cu-catalyzed Markovnikov hydroamination of alkynes (Scheme 10, bottom right). MacMillan and coworers used a similar but automated strategy in an approach they dubbed ‘‘accelerated serendipity’’ to rapidly identify a novel C–H arylation reaction.79 In an alternative protocol, Moran’s group focused on the simultaneous screening of numerous potential catalysts with the intention of identifying an optimal system. A dehydrative Friedel-Crafts reaction between p-methoxybenzyl alcohol (81) and mesitylene (82) was chosen as the test reaction (Scheme 11).80,81 This reaction was known to be catalyzed by strong acids but not by boronic or carboxylic acids. Their hypothesis was that a combination of a boronic acid and a bidentate O-based ligand could prove an effective catalyst in this reaction. To test this assumption, 12 boronic acids were chosen along with 12 potential ligands. Exposure of the mixture of 81 and 82 to these 24 compounds resulted in a positive reaction, giving a Friedel-Crafts adduct in 77% yield. To deconvolute which specific species were responsible for promoting the reaction, they arbitrarily divided

516

Chem 2, 502–524, April 13, 2017

Scheme 10. Systems-Based Reaction Discovery through Parallel Screening of Substrates Exposure of a mixture of substrates 64–80 to combinations of Earth-abundant transition metals and organic ligands resulted in the expedient discovery of several new reactions, including Ni-catalyzed hydroarylation of alkynes and Cu-catalyzed hydroamination of alkynes.

84a–84l and 85a–85l into two groups each: two sets of boronic acids with seven and five members (84a–84g and 84h–84l) and two sets of six oxygenated additives each (85a–85f and 85g–85l). They constructed four precatalyst pools by combining these sets and screened them all for activity. By far the highest yield (90%) was observed in one of the four pools (step 2, reactive set shown in blue in Scheme 11); this pool was then split into nine additional screens (step 3), and once again only one combination proved effective (91%)—all others fared much worse. Final narrowing of the screen allowed identification of a couple consisting of pentafluorophenylboronic acid (84a) and oxalic acid (85a) as the effective combination. This pool-split-test strategy allowed the use of just 31 screening reactions (14 to screen and 17 to deconvolute) to identify the appropriate catalyst. The classic serial approach to identifying this catalyst would have required 2016 separate reactions! A similar screening and deconvolution strategy was used to identify Ni-catalyzed C–H arylation81 and alkyne semireduction with exquisite selectivity control;82 further exploration of boron-mediated Friedel-Crafts reactivity resulted in the identification of an autocatalytic Friedel-Crafts reaction of tertiary aliphatic fluorides initiated by B(C6F5)3$H2O.83 The protocols described above for discovering new reactions present both a nontrivial conceptual and very practical departure from the traditional one-by-one screening of the conditions for new reactions. It could be argued, however, that they merely bridge the divide between the reductionist and systems-level approaches to reaction discovery; they are still strongly focused on the deconvolution of the specific active catalyst or reactive pair and are still intentionally limiting the

Chem 2, 502–524, April 13, 2017

517

Scheme 11. Systems-Level Screening of Catalysts for a Friedel-Crafts Reaction A mixture of reactants 81 and 82 was exposed first to a combination of 12 boronic acids (84a–84l) and 12 oxygen-based chelating ligands (85a–85l). Reaction ensued, giving 83 in 77% yield. From there, the ligands and boronic acids are split into four subsets (step 2), only one of which showed higher yield than the starting mixture. Further splitting of that subset into nine smaller ones (step 3) narrowed down the possible compositions of the active species. Final pairwise screening performed in step 4 identified 84a and 85a as the active catalysts.

518

Chem 2, 502–524, April 13, 2017

complexities of mixtures in, e.g., individual reaction wells. Although these steps can be justified by concerns for atom economy and broad usability of the discovered reactions, some steps toward a more systems-level approach seem to be within close reach. For example, it is already conceivable that a mixture of numerous catalysts and potential starting materials could be used as a pool for the discovery of multiple reactions at once. The practical execution of such reaction-discovery methods would depend on the clever choice of substrates and sufficiently high resolution of analytical methods used for probing the reaction mixture. In addition, coupling it with data-mining reactivity prediction software, such as Chematica,84 could limit its apparent randomness.

SINGLE MOLECULES AS SYSTEMS Although most of the previously discussed concepts of systems chemistry utilize complex mixtures of molecules, or study interactions between them, even individual molecules can be viewed as systems of significant complexity. After all, multiple functional groups on a complex molecule can often have desirable synergistic activity (e.g., in neighboring group participation) or undesirable cross-reactivity (which necessitates the use of protecting groups in synthesis). Recent years have brought some remarkable insights from several groups around the world into these complexities of individual molecules, allowing the development of unprecedented syntheses or functions that benefit from these insights. Arguably, such advances could not have been possible without systems-level consideration of these molecules, which largely dispensed with the reductionist notions of functional groups as isolated entities. Two very different examples will follow to illustrate this. In 2007, Baran’s group85 published a synthesis of marine natural products (86–89; Figure 1) that utilized no protecting groups whatsoever—a rare and quite broadly applicable feat. Protecting groups can be viewed as emblematic of the reductionist approach to chemical synthesis; they block the reactivity of all sites but one, so that it can react in isolation. Although successful in that task, protecting groups necessarily elongate the synthesis by two steps each and—more problematically—can introduce their own chemoselectivity issues into the syntheses of complex molecules.86,87 Approaching the syntheses of these targets with the explicit intention of avoiding protecting groups forced Baran and coworkers to develop new reactivity that avoided or productively exploited the reactivity that a protecting group would have masked. As a result, they identified new reactions, including an N–H-assisted procedure for C–H bond functionalization, as well as an unusual five-step tandem reaction; neither would have been discovered if protecting groups were used to shield traditionally sensitive functionalities. The importance of this work—and the reason for its inclusion in this review of systems chemistry—resides in its appreciation and ultimate utilization of complex chemical interactions among functional groups in the precursor molecules. As mentioned in the introduction, the more classic syntheses of complex molecules still (quite successfully) rely on the reductionist approach to functional groups. On the functional side of the spectrum, single-molecule machines offer insights into the sophisticated functions that can be realized in fully synthetic molecules once their chemistry is sufficiently well understood from a systems perspective. The work on molecular machines has been reviewed88,89 and honored by the 2016 Nobel Prize in Chemistry. Here, only one recent example will be highlighted. Leigh and coworkers90 recently presented the synthesis and operation of a unidirectional and chemically fueled motor. In a mechanically interlocked [2]catenane (Scheme 12), the

Chem 2, 502–524, April 13, 2017

519

N

H

HN

C

HN

86

N

C

87

H

Cl

H

C N

N O C

N H

Cl

NH H

88

89

Figure 1. Examples of Complex Marine Natural Products Synthesized without Protecting Groups Systems-level understanding of reactivity in precursors allowed Baran and coworkers to completely avoid the use of protecting groups in the syntheses of marine natural products 86–89.

smaller ring can reside on either of the two equivalent ‘‘stations’’ on the larger ring (deuteration of the bottom station serves only as a probe but does not affect the chemistry significantly). Two bulky fluorenylmethyloxycarbonyl (Fmoc) groups prevent it from translating to the other station, giving rise to two distinct configurations (90 and 91). Starting from either 90 or 91, cleavage of the two Fmoc groups proceeds with equal likelihood, giving co-conformers 92 and 93 (from 90) or 94 and 95 (from 91). The concentrations of reagents are chosen such that only one group can be cleaved at any given time. After cleavage, the smaller ring now can move back and forth between the two stations, equilibrating 92 with 94 and 93 with 95. Critical to the unidirectional operation of this molecular motor is the next step, in which the Fmoc group is reattached to the previously liberated –OH group. Here, the conditions are chosen to be different from the cleavage conditions; the bulky chiral catalyst shown in Scheme 12 ensures that the reattachment rate is much faster for the co-conformer in which the small ring is positioned away from the reactive site. Thus, only 95 (but not 93) will reattach the Fmoc group when the small ring is on the deuterated site, and similarly 92 (but not 94) will do so only when the small ring is on the non-deuterated site. Overall clockwise motion of the small ring around the big ring is achieved, despite the fact that there is Brownian back-and-forth motion during the intermediate stage when one of the –OH groups is free. The motor operates as long as Fmoc-Cl is added to it (via a syringe pump) as the chemical fuel. This work has been a brilliant illustration of a systems-level understanding of not just chemoselectivity but also reaction kinetics in a sophisticated functional system.

520

Chem 2, 502–524, April 13, 2017

O

catalyst / FmocCl Fmoc reattachment faster than in 94

N O OH N

H 3C

catalyst =

O

O

O

H N

O

O

NEt3 Fmoc cleavage

H 3C H3C

N

O

O NH HN H N

NEt3 Fmoc cleavage

NH HN O

CH3

O

90 O O O

H N

N

O

O

O

H N

O

N

O

O

O NH HN H N

O

O O

O

H N

N H

O

O O

D N

N H

D

O

O O

O

H N

N

O

OH

H N

O

N

N H

OH

N H

O

O

N H

OH

N H

D

O O

O NHDHN N

D

O

O

O

H N

N

NH HN O

93

O O

N D

O

O

95

O

O NHDHN O

O

92

O

H N

NH HN

O

94

OH

O

O

NH HN O

O

O NH HN H N

O

O

O

H N

N H

O

O

O

N H

N D

O

O

N H O

O

O

N H

D N D

O

O

NH HN

O

O

O

O

NEt3 Fmoc cleavage

NEt3 Fmoc cleavage 91 O

O

O NHDHN O

N H O

O

N H

N D

catalyst / FmocCl Fmoc reattachment faster than in 93

O

O NH HN O

O

Scheme 12. Chemically Fueled Molecular Motors Carefully designed reaction kinetics achieved unidirectional motion in a [2]catenane molecular motor. Starting from either 90 or 91, cleavage of a single Fmoc group proceeds to allow the movement of the smaller ring (shown in blue) along the larger ring to two stations (green) that have high affinity for it. Reattachment of the Fmoc group, however, proceeds much faster for the co-conformers 92 and 95, in which the blue ring stays away from the reactive –OH group. Because of this, the net overall motion of the smaller ring along the larger circuit is unidirectional, proceeding clockwise.

CONCLUSIONS AND OUTLOOK Although systems chemistry remains a broad and imperfectly defined area, some key features of this approach are beginning to emerge. First is the strong emphasis on the analysis of a complex system in its entirety rather than its parts. This methodology requires sophisticated analytical and computational tools, and some are being specifically developed for the purpose of analyzing complex mixtures. In the long term, this approach will also require some modification to the training of chemists to be better equipped to deal with the ‘‘messy’’; currently, the focus of synthetic training of young scientists is almost exclusively on the preparation of pure compounds. Systems chemists would still have a use for them but would also profit from an understanding of the reasons behind inherent complications encountered in common chemical reactions, as well as the challenges and opportunities that such complications present. Second, an understanding of thermodynamics is essential to the predictions of properties of chemical systems; however, the truly unexpected phenomena commonly involve kinetically controlled processes, which are both more difficult to predict a priori and treat mathematically.91 Further complicating things is the fact that kinetic

Chem 2, 502–524, April 13, 2017

521

parameters need to be understood not only for the chemical reactions but also for physical processes such as component diffusion. With the increased use of flow systems in synthetic chemistry,92 some control of kinetic and thermodynamic factors can be achieved, allowing increased exploration of far-from-equilibrium systems.93 Third, systems chemistry in homogeneous solutions is just the tip of the iceberg; understanding behavior across phases could yield fascinating new insights into the isolation and controlled communication of two or more complex systems. Apart from practical interest, such work would have potential ramifications in studies of compartmentalization and drying-wetting cycles of relevance to the origins of life. Finally, although much emphasis in the study of complex mixtures of molecules has been on the ability to generate new and unusual forms of matter (i.e., molecules and complexes), chemical systems are also very information-rich entities. An ability to reproducibly deconvolute such information is already finding applications in reaction discovery, but many more opportunities are yet to be explored. My group is currently exploring one of these directions: the use of chemical systems for information storage and computation.

ACKNOWLEDGMENTS Over the past 8 years, research in my labs was performed by a number of talented students and was generously funded by the University of Houston and its Grants to Advance and Enhance Research (GEAR) program, the National Science Foundation (award CHE-1151292), and the Welch Foundation (award E-1768). I am a Cottrell Scholar of the Research Corporation for Science Advancement.

REFERENCES AND NOTES 1. (1971). Webster’s Third New International Dictionary and Seven Language Dictionary (G&C Merriam Co.). 2. Whitesides, G.M., and Ismagilov, R.F. (1999). Complexity in chemistry. Science 284, 89–92. 3. Stankiewicz, J., and Eckardt, L.H. (2006). Chembiogenesis 2005 and systems chemistry workshop. Angew. Chem. Int. Ed. 45, 342–344. 4. Mattia, E., and Otto, S. (2015). Supramolecular systems chemistry. Nat. Nanotechnol. 10, 111–119. 5. Peyralans, J.J.P., and Otto, S. (2009). Recent highlights in systems chemistry. Curr. Opin. Chem. Biol. 13, 705–713. 6. Ludlow, R.F., and Otto, S. (2008). Systems chemistry. Chem. Soc. Rev. 37, 101–108. 7. Grzybowski, B., Otto, S., and Philp, D. (2014). Systems chemistry: a web themed issue. Chem. Commun. 50, 14924–14925. 8. von Kiedrowski, G., Otto, S., and Herdewijn, P. (2010). Welcome home, systems chemists! J. Syst. Chem. 1, 1–6. 9. Darley, V. (1994). Emergent phenomena and complexity. In Artificial Life IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, R. Brooks and P. Maes, eds. (MIT Press), pp. 411–416. 10. Reek, J.N.H., and Otto, S. (2010). Dynamic Combinatorial Chemistry (Wiley-VCH). 11. Corbett, P.T., Leclaire, J., Vial, L., West, K.R., Wietor, J.-L., Sanders, J.K.M., and Otto, S.

522

Chem 2, 502–524, April 13, 2017

(2006). Dynamic combinatorial chemistry. Chem. Rev. 106, 3652–3711. 12. Li, J., Nowak, P., and Otto, S. (2013). Dynamic combinatorial libraries: from exploring molecular recognition to systems chemistry. J. Am. Chem. Soc. 135, 9222–9239.

19. Brisig, B., Sanders, J.K.M., and Otto, S. (2003). Selection and amplification of a catalyst from a dynamic combinatorial library. Angew. Chem. Int. Ed. 42, 1270–1273. 20. Dydio, P., Breuil, P.-A.R., and Reek, J.N.H. (2013). Dynamic combinatorial chemistry in chemical catalysis. Isr. J. Chem. 53, 61–74.

13. Meyer, C.D., Forgan, R.S., Chichak, K.S., Peters, A.J., Tangchaivang, N., Cave, G.W.V., Khan, S.I., Cantrill, S.J., and Stoddart, J.F. (2010). The dynamic chemistry of molecular Borromean rings and Solomon knots. Chem. Eur. J. 16, 12570–12581.

21. Grote, Z., Scopelliti, R., and Severin, K. (2003). Adaptive behavior of dynamic combinatorial libraries generated by assembly of different building blocks. Angew. Chem. Int. Ed. 42, 3821–3825.

14. Saha, M.L., and Schmittel, M. (2012). Degree of molecular self-sorting in multicomponent systems. Org. Biomol. Chem. 10, 4651–4684.

22. Severin, K. (2004). The advantage of being virtual–target-induced adaptation and selection in dynamic combinatorial libraries. Chem. Eur. J. 10, 2565–2580.

15. Mahata, K., Saha, M.L., and Schmittel, M. (2010). From an eight-component self-sorting algorithm to a trisheterometallic scalene triangle. J. Am. Chem. Soc. 132, 15933–15935.

23. Corbett, P.T., Otto, S., and Sanders, J.K.M. (2004). Correlation between host-guest binding and host amplification in simulated dynamic combinatorial libraries. Chem. Eur. J. 10, 3139–3143.

16. Saha, M.L., and Schmittel, M. (2013). From 3-fold completive self-sorting of a ninecomponent library to a seven-component scalene quadrilateral. J. Am. Chem. Soc. 135, 17743–17746. 17. Otto, S., Furlan, R.L.E., and Sanders, J.K.M. (2002). Selection and amplification of hosts from dynamic combinatorial libraries of macrocyclic disulfides. Science 297, 590–593. 18. Zhang, C., Wang, Q., Long, H., and Zhang, W. (2011). A highly C70 selective shape-persistent rectangular prism constructed through onestep alkyne metathesis. J. Am. Chem. Soc. 133, 20995–21001.

24. Ludlow, R.F., Liu, J., Li, H., Roberts, S.L., Sanders, J.K.M., and Otto, S. (2007). Host-guest binding constants can be estimated directly from the product distributions of dynamic combinatorial libraries. Angew. Chem. Int. Ed. 46, 5762–5764.  (2014). 25. Ji, Q., Lirag, R.C., and Miljanic, O.S. Kinetically controlled phenomena in dynamic combinatorial libraries. Chem. Soc. Rev. 43, 1873–1884. 26. Vongvilai, P., Angelin, M., Larsson, R., and Ramstrom, O. (2007). Dynamic combinatorial resolution: direct asymmetric lipase-mediated

screening of a dynamic nitroaldol library. Angew. Chem. Int. Ed. 46, 948–950. 27. Vongvilai, P., and Ramstro¨m, O. (2009). Dynamic asymmetric multicomponent resolution: lipase-mediated amidation of a double dynamic covalent system. J. Am. Chem. Soc. 131, 14419–14425.

44. Allen, V.C., Robertson, C.C., Turega, S.M., and Philp, D. (2010). A simple network of synthetic replicators can perform the logical OR operation. Org. Lett. 12, 1920–1923. 45. Bissette, A.J., and Fletcher, S.R. (2013). Mechanisms of autocatalysis. Angew. Chem. Int. Ed. 52, 12800–12826.

28. Wu, A., and Isaacs, L. (2003). Self-sorting: the exception or the rule? J. Am. Chem. Soc. 125, 4831–4835.

46. Showalter, K., and Epstein, I.R. (2015). From chemical systems to systems chemistry: patterns in space and time. Chaos 25, 097613.

29. Safont-Sempere, M.M., Fernandez, G., and Wu¨rthner, F. (2011). Self-sorting phenomena in complex supramolecular systems. Chem. Rev. 111, 5784–5814.

47. For a recent example, see Pramanik, S., and Aprahamian, I. (2016). Hydrazone switch-based negative feedback loop. J. Am. Chem. Soc. 138, 15142–15145.

 (2011). Kinetic 30. Osowska, K., and Miljanic, O.S. and thermodynamic self-sorting in synthetic systems. Synlett, 1643–1648.

48. Epstein, I.R., Vanag, V.K., Balazs, A.C., Kuksenok, O., Dayal, P., and Bhattacharya, A. (2012). Chemical oscillators in structured media. Acc. Chem. Res. 45, 2160–2168.

 (2011). 31. Osowska, K., and Miljanic, O.S. Oxidative kinetic self-sorting of a dynamic imine library. J. Am. Chem. Soc. 133, 724–727.  (2011). Self32. Osowska, K., and Miljanic, O.S. sorting of dynamic imine libraries during distillation. Angew. Chem. Int. Ed. 50, 8345– 8349.  33. Lirag, R.C., Osowska, K., and Miljanic, O.S. (2012). Precipitation-driven self-sorting of imines. Org. Biomol. Chem. 10, 4847–4850.  (2015). 34. Hsu, C.-W., and Miljanic, O.S. Adsorption-driven self-sorting of dynamic imine libraries. Angew. Chem. Int. Ed. 54, 2219– 2222.  (2013). Distillative self35. Ji, Q., and Miljanic, O.S. sorting of dynamic ester libraries. J. Org. Chem. 78, 12710–12716. 36. Hafezi, N., and Lehn, J.-M. (2012). Adaptation of dynamic covalent systems of imine constituents to medium change by component redistribution under reversible phase separation. J. Am. Chem. Soc. 134, 12861– 12868. 37. Grommet, A.B., Bolliger, J.L., Browne, C., and Nitschke, J.R. (2015). A triphasic sorting system: coordination cages in ionic liquids. Angew. Chem. Int. Ed. 54, 15100–15104. 38. Nitschke, J.R. (2007). Construction, substitution, and sorting of metallo-organic structures via subcomponent self-assembly. Acc. Chem. Res. 40, 103–112. 39. Schaufelberger, F., and Ramstro¨m, O. (2016). Kinetic self-sorting of dynamic covalent catalysts with systemic feedback regulation. J. Am. Chem. Soc. 138, 7836–7839. 40. Patzke, V., and von Kiedrowski, G. (2007). Selfreplicating systems. ARKIVOC, 293–310. 41. del Amo, V., and Philp, D. (2010). Integrating replication-based selection strategies in dynamic covalent systems. Chem. Eur. J. 16, 13304–13318. 42. del Amo, V., Slawin, A.M.Z., and Philp, D. (2008). Manipulating replication processes within a dynamic covalent framework. Org. Lett. 10, 4589–4592. 43. Sadownik, J.W., and Philp, D. (2008). A simple synthetic replicator amplifies itself from a dynamic reagent pool. Angew. Chem. Int. Ed. 47, 9965–9970.

49. Tsogoeva, S.B. (2010). Organoautocatalysis: challenges for experiment and theory. J. Syst. Chem. 1, 1–7.

zhabotinsky reaction in an aerosol OT microemulsion. Phys. Rev. Lett. 87, 228301. 61. Bottero, I., Huck, J., Kosikova, T., and Philp, D. (2016). A synthetic replicator drives a propagating reaction–diffusion front. J. Am. Chem. Soc. 138, 6723–6726. 62. Islam, S., and Powner, M. (2017). Prebiotic systems chemistry—complexity overcoming clutter. Chem 2, this issue, 470–501. 63. Ruiz-Mirazo, K., Briones, C., and de la Escosura, A. (2014). Prebiotic systems chemistry: new perspectives for the origins of life. Chem. Rev. 114, 285–366. 64. Sojo, V., Barry, H., Whicher, A., Camprubı´, E., and Lane, N. (2016). The origin of life in alkaline hydrothermal vents. Astrobiology 16, 181. 65. Boutlerow, M.A. (1861). Formation synthe´tique d’une substance sucre´e. Compt. Rend. 53, 145–147. 66. Breslow, R. (1959). On the mechanism of the formose reaction. Tetrahedron Lett. 1, 22–26.

50. Fujisawa, H., Nakagawa, T., and Mukaiyama, T. (2004). Product-catalyzed aldol reaction between trimethylsilyl enolates and aldehydes. Adv. Synth. Catal. 346, 1241–1246.

67. Ricardo, A., Carrigan, M.A., Olcott, A.N., and Benner, S.A. (2004). Borate minerals stabilize ribose. Science 303, 196.

51. Soai, K., Shibata, T., Morioka, H., and Choji, K. (1995). Asymmetric autocatalysis and amplification of enantiomeric excess of a chiral molecule. Nature 378, 767–768.

68. Lambert, J.B., Gurusamy-Thangavelu, S.A., and Ma, K. (2010). The silicate-mediated formose reaction: bottom-up synthesis of sugar silicates. Science 327, 984–986.

52. Soai, K., Shibata, T., and Sato, I. (2000). Enantioselective automultiplication of chiral molecules by asymmetric autocatalysis. Acc. Chem. Res. 33, 382–390. 53. Kawasaki, T., Matsumura, Y., Tsutsumi, T., Suzuki, K., Ito, M., and Soai, K. (2009). Asymmetric autocatalysis triggered by carbon isotope (13C/12C) chirality. Science 324, 492–495. 54. Mauksch, M., Tsogoeva, S.B., Martynova, I.M., and Wei, S. (2007). Evidence of asymmetric autocatalysis in organocatalytic reactions. Angew. Chem. Int. Ed. 46, 393–396. 55. Mauksch, M., Wei, S., Freund, M., Zamfir, A., and Tsogoeva, S.B. (2010). Spontaneous mirror symmetry breaking in the aldol reaction and its potential relevance in prebiotic chemistry. Orig. Life Evol. Biosph. 40, 79–91. 56. Mauksch, M., Tsogoeva, S.B., Martynova, I.M., and Wei, S. (2007). Demonstration of spontaneous chiral symmetry breaking in asymmetric Mannich and Aldol reactions. Chirality 19, 816–825. 57. Semenov, S.N., Kraft, L.J., Ainla, A., Zhao, M., Baghbanzadeh, M., Campbell, V.E., Kang, K., Fox, J.M., and Whitesides, G.M. (2016). Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions. Nature 537, 656–660. 58. Turing, A.M. (1952). The chemical basis of morphogenesis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 237, 37–72. 59. Belousov, B.P. (1958). Periodicheski Deistvuyushchaya Reaktsia I Ee Mechanism [in Russian]. Sbornik Referatov Po Radiatsionni Meditsine (Medgiz), pp. 145–147. 60. Vanag, V.K., and Epstein, I.R. (2001). Pattern formation in a tunable medium: the belousov-

69. Powner, M.W., Gerland, B., and Sutherland, J.D. (2009). Synthesis of activated pyrimidine ribonucleotides in prebiotically plausible conditions. Nature 459, 239–242. 70. Szostak, J.W. (2009). Origins of life: systems chemistry on early earth. Nature 459, 171–172. 71. Mullen, L.B., and Sutherland, J.D. (2007). Simultaneous nucleotide activation and synthesis of amino acid amides by a potentially prebiotic multi-component reaction. Angew. Chem. Int. Ed. 46, 8063–8066. 72. Patel, B.H., Percivalle, C., Ritson, D.J., Duffy, C.D., and Sutherland, J.D. (2015). Common origins of RNA, protein and lipid precursors in a cyanosulfidic protometabolism. Nat. Chem. 7, 301–307. 73. Bracher, P.J. (2015). Primordial soup that cooks itself. Nat. Chem. 7, 273–274. 74. Zhang, X.V., and Martin, S.T. (2006). Driving parts of Krebs cycle in reverse through mineral photochemistry. J. Am. Chem. Soc. 128, 16032–16033. 75. Smith, E., and Morowitz, H.J. (2004). Universality in intermediary metabolism. Proc. Natl. Acad. Sci. USA 101, 13168–13173. 76. Collins, K.D., Gensch, T., and Glorius, F. (2014). Contemporary screening approaches to reaction discovery and development. Nat. Chem. 6, 859–871. 77. Gasparini, G., Molin, M.D., and Prins, L.J. (2010). Dynamic approaches towards catalyst discovery. Eur. J. Org. Chem. 2429–2440. 78. Robbins, D.W., and Hartwig, J.F. (2011). A simple, multidimensional approach to highthroughput discovery of catalytic reactions. Science 333, 1423–1427.

Chem 2, 502–524, April 13, 2017

523

79. McNally, A., Prier, C.K., and MacMillan, D.W.C. (2011). Discovery of an a-amino C–H arylation reaction using the strategy of accelerated serendipity. Science 334, 1114–1117. 80. Richmond, E., and Moran, J. (2016). Harnessing complex mixtures for catalyst discovery. Synlett 27, 2637–2643. 81. Wolf, E., Richmond, E., and Moran, J. (2015). Identifying lead hits in catalyst discovery by screening and deconvoluting complex mixtures of catalyst components. Chem. Sci. 6, 2501–2505. 82. Richmond, E., and Moran, J. (2015). Ligand control of E/Z selectivity in nickel-catalyzed transfer hydrogenative alkyne semireduction. J. Org. Chem. 80, 6922–6929. 83. Dryzhakov, M., and Moran, J. (2016). Autocatalytic Friedel–Crafts reactions of tertiary aliphatic fluorides initiated by B(C6F5)3$H2O. ACS Catalysis 6, 3670–3673.

524

Chem 2, 502–524, April 13, 2017

84. Kowalik, M., Gothard, C.M., Drews, A.M., Gothard, N.A., Weckiewicz, A., Fuller, P.E., Grzybowski, B.A., and Bishop, K.J.M. (2012). Parallel optimization of synthetic pathways within the network of organic chemistry. Angew. Chem. Int. Ed. 51, 7928– 7932. 85. Baran, P.S., Maimone, T.J., and Richter, J.M. (2007). Total synthesis of marine natural products without using protecting groups. Nature 446, 404–408. 86. Young, I.S., and Baran, P.S. (2009). Protectinggroup-free synthesis as an opportunity for invention. Nat. Chem. 1, 193–205.

89. Balzani, V., Credi, A., Raymo, F.M., and Stoddart, J.F. (2000). Artificial molecular machines. Angew. Chem. Int. Ed. 39, 3348– 3391. 90. Wilson, M.R., Sola, J., Carlone, A., Goldup, S.M., Lebrasseur, N., and Leigh, D.A. (2016). An autonomous chemically fuelled small-molecule motor. Nature 534, 235–241. 91. Zhou, Y., Li, L., Ye, H., Zhang, L., and You, L. (2016). Quantitative reactivity scales for dynamic covalent and systems chemistry. J. Am. Chem. Soc. 136, 381–389.

87. Hoffmann, R.W. (2006). Protecting-group-free synthesis. Synthesis, 3531–3541.

92. Wegner, J., Ceylan, S., and Kirschning, A. (2012). Flow chemistry – a key enabling technology for (multistep) organic synthesis. Adv. Synth. Catal. 354, 17–57.

88. Erbas-Cakmak, S., Leigh, D.A., McTernan, C.T., and Nussbaumer, A.L. (2015). Artificial molecular machines. Chem. Rev. 115, 10081– 10206.

93. Armao, J.J., and Lehn, J.-M. (2016). Adaptive chemical networks under non-equilibrium conditions: the evaporating droplet. Angew. Chem. Int. Ed. 55, 13450–13454.