Quantum mechanical investigations of organocatalysis: mechanisms, reactivities, and selectivities

Additive and Emergent Catalytic Properties of Dimeric Unnatural Amino Acid Derivatives: Aldol and Conjugate Additions

Different densely substituted L- and D-proline esters were prepared by asymmetric (3+2) cycloaddition reactions catalyzed by conveniently selected EhuPhos chiral ligands. The γ-nitro-2-alkoxycarbonyl pyrrolidines thus obtained in either their endo or exo forms were functionalized and coupled to yield the corresponding γ-dipeptides. The catalytic properties of these latter dimers were examined using aldol and conjugate additions as case studies. When aldol reactions were analyzed, an additive behavior in terms of stereocontrol was observed on going from the monomers to the dimers. In contrast, in the case of the conjugate additions between ketones and nitroalkenes, the monomers did not catalyze this reaction, whereas the different γ-dipeptides promoted the formation of the corresponding Michael adducts. Therefore, in this latter case emergent catalytic properties were observed for these novel γ-dipeptides based on unnatural proline derivatives. Under certain conditions stoichiometric amounts of ketone, acid and nitroalkene), formation of N-acyloxy-2-oxooctahydro-1H-indoles was observed.

Chiral anthranilic pyrrolidine as custom-made amine catalyst for enantioselective Michael reaction of nitroalkenes with carbonyl compounds

A chiral anthranilic pyrrolidine catalyst as a custom-made amine-catalyst was developed for the enantio- and diastereo selective Michael reaction of nitroalkenes with carbonyl compounds. In particular, a peptide-like catalyst in which an α-amino acid is attached to the anthranilic acid skeleton induced the high stereoselectivity of the reaction with aldehydes. Studies of the reaction mechanism indicated that the catalyst exhibits a divergent stereocontrol in the reaction, namely, steric control by a 2-substituted group on the catalyst and hydrogen-bonding control by a carboxylic acid group on the catalyst.

Density Functional Theory Study on the 193Ir Mössbauer Spectroscopic Parameters of Vaska's Complexes and Their Oxidative Adducts

In the present study, density functional theory (DFT) calculation was applied to Vaska's complexes of formula trans-[Ir^IX(CO)(PPh₃)₂] and their oxidative adducts with small molecules (YZ) including H₂, i.e., trans-[Ir^IIIClYZ(CO)(PPh₃)₂], to successfully correlate the electronic states of the complexes with the corresponding ¹⁹³Ir Mössbauer spectroscopic parameters. After confirming the reproducibility of the DFT methods for elucidating the equilibrium structures and ¹⁹³Ir Mössbauer isomer shifts of the octahedral Ir complexes, the isomer shifts and quadrupole splitting values of Vaska's complexes and their oxidative adducts were calculated. A bond critical point analysis revealed that the tendency in the isomer shifts was correlated with the strength of the covalent interaction in the coordination bonds. In an electric field gradient (EFG) analysis of the oxidative adducts, the sign of the principal axis was found to be positive for the complex with YZ = Cl₂ and negative for the complex with YZ = H₂. This reversal of the sign of the EFG principal axis was caused by the difference in the electron density distribution for the coordination bonds between Ir and YZ, according to a density of states analysis.

Diaminomethylenemalononitrile as a Chiral Single Hydrogen Bond Catalyst: Application to Enantioselective Conjugate Addition of α-Branched Aldehydes

An improved diaminomethylenemalononitrile organocatalyst, bearing a N,N-disubstituted structure, promoted enantioselective conjugate addition reaction of α-branched aldehydes with vinyl sulfone, affording adducts with excellent enantioselectivities (up to 96% ee). Mechanistic studies revealed that the diaminomethylenemalononitrile motif holds the vinyl sulfone substrate using a single hydrogen bond accompanied by multiple weak interactions, including electrostatic C-H⋅⋅⋅O interactions.

Towards Data-driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

Asymmetric hydrogenation of olefins is one of the most powerful asymmetric transformations in molecular synthesis. Although several privileged catalyst scaffolds are available, the catalyst development for asymmetric hydrogenation is still a time- and resource-consuming process due to the lack of predictive catalyst design strategy. Targeting the data-driven design of asymmetric catalysis, we herein report the development of a standardized database that contains the detailed information of over 12000 literature asymmetric hydrogenations of olefins. This database provides a valuable platform for the machine learning applications in asymmetric catalysis. Based on this database, we developed a hierarchical learning approach to achieve predictive machine leaning model using only dozens of enantioselectivity data with the target olefin, which offers a useful solution for the few-shot learning problem and will facilitate the reaction optimization with new olefin substrate in catalysis screening.

Data Science Meets Physical Organic Chemistry

ConspectusAt the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate.In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔG⧧ = -RT ln[(S)/(R)], where (S) and (R) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way.Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design.In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

Trends in computational molecular catalyst design

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved.

Regio-controllable Cobalt-Catalyzed Sequential Hydrosilylation/Hydroboration of Arylacetylenes

Regiodivergent addition reactions provide straightforward and atom-economic approaches to access different regioisomers. However, the regio-chemistry control to access all the possible results is still challenging especially for the reaction involving multiple addition steps. Herein, we reported regio-controllable cobalt-catalyzed sequential hydrosilylation/hydroboration of arylacetylenes, delivering all the possible regio-outcomes with high regioselectivities (up to >20/1 rr for all the cases). Each regioisomer of value-added silylboronates could be efficiently and regioselectively obtained from the same materials. The adjustment of the ligands of cobalt catalysts combined with dual catalysis relay strategy is the key to achieve regio-chemistry control. This regio-controllable research might inspire the exploration of the diversity-oriented synthesis that involves multiple additions and provide full sets of regioisomers of other synthetic useful molecules.

Accelerating the optimization of enzyme-catalyzed synthesis conditions via machine learning and reactivity descriptors

Enzyme-catalyzed synthesis reactions are of crucial importance for a wide range of applications. An accurate and rapid selection of optimal synthesis conditions is crucial and challenging for both human knowledge and computer predictions. In this work, a new scenario, which combines a data-driven machine learning (ML) model with reactivity descriptors, is developed to predict the optimal enzyme-catalyzed synthesis conditions and the reaction yield. Fourteen reactivity descriptors in total are constructed to describe 125 reactions (classified into five categories) included in different reaction mechanisms. Nineteen ML models are developed to train the dataset and the Quadratic support vector machine (SVM) model is found to exhibit the best performance. The Quadratic SVM model is then used to predict the optimal reaction conditions, which are subsequently used to obtain the highest yield among 109 200 reaction conditions with different molar ratios of substrates, solvents, water contents, enzyme concentrations and temperatures for each reaction. The proposed protocol should be generally applicable to a diverse range of chemical reactions and provides a black-box evaluation for optimizing the reaction conditions of organic synthesis reactions.

Electrostatic Switching of Stereoselectivity in Aldol Reactions

Density functional theory (DFT) has been employed in predicting the enantioselectivity of the aldol reaction between acetone and p-nitrobenzaldehyde catalyzed by proline and its derivatives Me₂bdc-Pro (bdc = 1,4-benzenedicarboxylate) and Me₂bpdc-Pro (bpdc = 4,4'-biphenyldicarboxylate). For each catalyst, our computationally predicted values at the M062X/6-31+G(d) level of theory with the SMD solvent model are in excellent agreement with experimental results reported in the literature. Electron-donating and electron-withdrawing groups (viz., SO₃^-, NMe₂, SO₃H, and NMe₃⁺) were installed at the C4 position of the proline-based catalysts to study the impact of electrostatic effects on stereoselectivity. The electron-donating groups decrease and even invert the enantioselectivity, while the electron-withdrawing ones increase it. Enantiomeric excesses in the range of 49-71 and 59-68% are predicted for Me₂bdc-Pro and Me₂bpdc-Pro catalysts with the electron-withdrawing SO₃H and NMe₃⁺ installed respectively, values much higher than those of the corresponding unmodified catalysts. More interestingly, enantiomeric excesses decrease and, in the case of SO₃^-, are even inverted in favor of the other enantiomer when the electron-donating groups are installed. These results highlight the importance of electrostatic effects, and polar effects more generally, in optimal organocatalyst design for stereoselective C-C bond-forming reactions.

Machine Learning for Chemical Reactions

Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to computational platforms for reaction planning. ML-based techniques can be particularly relevant for problems involving both computation and experiments. For one, Bayesian inference is a powerful approach to develop models consistent with knowledge from experiments. Second, ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques, and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, in both research and academic teaching.

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol-1 were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information.

Synthesis of Functionalized Bicyclo[2.2.2]octan-2-ones Skeleton via Tandem Process of Amino Acid Involved Formal [4 + 2] and Decarboxylative-Mannich Sequence

An efficient approach to a functionalized bicyclo[2.2.2]octan-2-one scaffold has been developed through a one-pot cascade process including amino acid involved successive Michael addition and decarboxylative-Mannich sequence. Starting from α,β-unsaturated ketones and amino acids, a series of desired products 7a-7m and 8a-8o were obtained with moderate yields. In addition, the tandem process was reasonably explained by the results of DFT calculations.

Emerging computational approaches for the study of regio- and stereoselectivity in organic synthesis

Computational chemistry has become important in organic synthesis as it provides a detailed understanding of molecular structures and properties and detailed reaction mechanisms.

Unusual temperature-sensitive protonation behaviour of 4-(dimethylamino)pyridine

An unusually strong impact of temperature on the protonation degree of DMAP in strong acid solutions has been found.

Flow synthesis kinetics for lomustine, an anti-cancer active pharmaceutical ingredient

An original reaction mechanism and kinetic parameter estimation has been achieved for lomustine, an anti-cancer active pharmaceutical ingredient (API).

Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next

Different subset selection methods are examined to guide catalyst selection in optimization campaigns. Error assessment methods are used to quantitatively inform selection of new catalyst candidates from in silico libraries of catalyst structures.

Bicyclic Guanidine-Catalyzed Asymmetric Cycloaddition Reaction of Anthrones-Bifunctional Binding Modes and Origin of Stereoselectivity

We report a computational analysis of the [5,5] bicyclic guanidine-catalyzed asymmetric cycloaddition reaction of anthrones. Based on extensive conformational search of key intermediates and transition states on the potential energy surface and density functional theory calculations, we studied five plausible binding modes between the guanidine catalyst and substrates for this reaction. Our results indicate that the most favorable pathway is a stepwise conjugate addition-Aldol sequence via the dual hydrogen-bond binding mode. The predicted level of enantioselectivity is in good agreement with experimental values. Trends in variation of substrates and catalysts have also been reproduced by our calculations. Decomposition analysis revealed the significance of aromatic interactions in stabilizing the key enantioselectivity-determining transition state structures.

Direct Amine-Catalyzed Enantioselective Synthesis of Pentacyclic Dibenzo[b,f][1,4]oxazepine/Thiazepine-Fused Isoquinuclidines along with DFT Calculations

A direct protocol for the asymmetric synthesis of dibenzoxazepine/thiazepine-fused [2.2.2] isoquinuclidines is developed. The reaction proceeds through a proline-catalyzed direct Mannich reaction followed by an intramolecular aza-Michael cascade sequence between 2-cyclohexene-1-one and various tricyclic imines, like dibenzoxazepines/thiazepines, as an overall [4 + 2] aza-Diels-Alder reaction. A series of pentacyclic isoquinuclidines have been prepared, with complete endo-selectivity, in good to high yields and excellent enantioselectivity (>99:1). Density functional theory (DFT) calculations further support the observed high stereochemical outcome of the reaction.

The Effect of Solvent-Substrate Noncovalent Interactions on the Diastereoselectivity in the Intramolecular Carbonyl-Ene and the Staudinger [2 + 2] Cycloaddition Reactions

Noncovalent interactions (NCIs) have been identified as important contributing factors for determining selectivity in organic transformations. However, cases where NCIs between solvents and substrates are responsible for a major extent for determining selectivity are rare. The current computational study with density functional theory identifies two important transformations where this is the case: the intramolecular carbonyl-ene reaction and the Staudinger [2 + 2] cycloaddition reaction. In both cases, the role of explicit solvent molecules interacting noncovalently with the substrate has been taken into account. Calculations indicate that the diastereomeric ratio would be 95.0:5.0 for the formation of tricyclic tetrahydrofuran diastereomers via the intramolecular carbonyl-ene reaction and 94.0:6.0 for the formation of the triflone diastereomers via the Staudinger [2 + 2] cycloaddition reaction, which corroborates with the experiment. Interestingly, in both the cases, the calculations indicate that noninclusion of explicit solvent molecules would lead to only a small difference between the competing transition states, which leads to the conclusion that solvent-substrate NCIs are the major cause for diastereoselectivity in both the cases considered.

Computational investigations of NHC-backbone configurations for applications in organocatalytic umpolung reactions

Density functional theory (DFT) and multiconfigurational self-consistent field theory (MCSCF) methods are employed to investigate variation of the electronic properties of various N-heterocyclic carbenes. Alterations to the backbone by increased or decreased conjugation, heteroatom substitution in the NHC ring, and electron-donating or -withdrawing backbone substituents are modeled. The MCSCF calculations show extensive delocalization of both the highest occupied and lowest unoccupied molecular orbitals for NHCs with polymerizable backbone substituents. The free energies of the intermediates and transition structures for benzoin condensation are also shown to be sensitive to substitution of the NHC backbone. Taken together, these results imply great sensitivity of the reactivity of poly(NHC) catalysts to backbone modification at this moiety. Implications with respect to enhancement of poly(NHC)s employed in umpolung catalysis are discussed.

Phosphine-Catalyzed (4+1) Annulation: Rearrangement of Allenylic Carbamates to 3-Pyrrolines through Phosphonium Diene Intermediates

We have developed a phosphine-catalyzed (4+1) annulative rearrangement for the preparation of 3-pyrrolines from allenylic carbamates via phosphonium diene intermediates. We employed this methodology to synthesize an array of 1,3-disubstituted- and 1,2,3-trisubstituted-3-pyrrolines, including the often difficult to prepare 2-alkyl variants. A mechanistic investigation employing allenylic acetates and mononucleophiles unexpectedly unveiled that a phosphine-catalyzed (4+1) reaction for the construction of cyclopentene products, previously reported by Tong, might not occur through a phosphonium diene, as had been proposed, but rather through multiple mechanisms working in concert. Consequently, our phosphine-catalyzed rearrangement is most likely the first transformation to involve the unequivocal formation of a phosphonium diene intermediate along the reaction pathway. To demonstrate the synthetic utility of this newly developed reaction, we have completed concise formal syntheses of the pyrrolizidine alkaloids (±)-trachelanthamidine and (±)-supinidine.

Mechanistic Studies of the TRIP-Catalyzed Allylation with Organozinc Reagents

3,3-Bis(2,4,6-triisopropylphenyl)-1,1-binaphthyl-2,2-diyl hydrogenphosphate (TRIP) catalyzes the asymmetric allylation of aldehydes with organozinc compounds, leading to highly valuable structural motifs, like precursors to lignan natural products. Our previously reported mechanistic proposal relies on two reaction intermediates and requires further investigation to really understand the mode of action and the origins of stereoselectivity. Detailed ab initio calculations, supported by experimental data, render a substantially different mode of action to the allyl boronate congener. Instead of a Brønsted acid-based catalytic activation, the chiral phosphate acts as a counterion for the Lewis acidic zinc ion, which provides the activation of the aldehyde.

Prediction of NHC-catalyzed chemoselective functionalizations of carbonyl compounds: a general mechanistic map

Generally, N-heterocyclic carbene (NHC) complexed with carbonyl compounds would transform into several important active intermediates, i.e., enolates, Breslow intermediates, or acylazolium intermediates, which act as either a nucleophile (Nu) or an electrophile (E) to react with the other E/Nu partner. Hence, the key to predicting the origin of chemoselectivity is to compute the activity (i.e., electrophilic index ω for E and nucleophilic index N for Nu) and stability of the intermediates and products, which are suggested in a general mechanistic map of these reactions. To support this point, we selected and studied different cases of the NHC-catalyzed reactions of carbonyl compounds in the presence of a base and/or an oxidant, in which multiple possible pathways involving acylazolium, enolate, Breslow, and α,β-unsaturated acylazolium intermediates were proposed and a novel index ω + N of the E and Nu partners was employed to exactly predict the energy barrier of the chemoselective step in theory. This work provides a guide for determining the general principle behind organocatalytic reactions with various chemoselectivities, and suggests a general application of the reaction index in predicting the chemoselectivity of the nucleophilic and electrophilic reactions.

A novel index ω + N can be used to predict the chemoselectivity according to the general NHC-catalyzed reaction mechanism.

Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis

Modern, enantioselective catalyst development is driven largely by empiricism. Although this approach has fostered the introduction of most of the existing synthetic methods, it is inherently limited by the skill, creativity, and chemical intuition of the practitioner. Herein, we present a complementary approach to catalyst optimization in which statistical methods are used at each stage to streamline development. To construct the optimization informatics workflow, a number of critical components had to be subjected to rigorous validation. First, the critically important molecular descriptors were validated in two case studies to establish the importance of conformation-dependent molecular representations. Next, with a large data set available, it was possible to investigate the amount of data necessary to make predictive models with different modeling methods. Given the commercial availability of many catalyst structures, it was possible to compare models generated with algorithmically selected training sets and commercially available training sets. Finally, the augmentation of limited data sets is demonstrated in a method informed by unsupervised learning to restore the accuracy of the generated models.