Computational Insights into Privileged Stereocontrolling Interactions Involving Chiral Phosphates and Iminium Intermediates

Computational methods for investigating organic radical species

Computational analysis of organic radical species presents significant challenges. This study compares the efficacy of various DFT and wavefunction methods in predicting radical stabilisation energies, bond dissociation energies, and redox potentials for organic radicals. The hybrid meta-GGA M062X-D3(0), and the range-separated hybrids ωB97M-V and ωB97M-D3(BJ) emerged as the most reliable functionals, consistently providing accurate predictions across different basis sets including 6-311G**, cc-pVTZ, and def2-TZVP.

Mechanism to model: a physical organic chemistry approach to reaction prediction

The application of mechanistic generalizations is at the core of chemical reaction development and application. These strategies are rooted in physical organic chemistry where mechanistic understandings can be derived from one reaction and applied to explain another. Over time these techniques have evolved from rationalizing observed outcomes to leading experimental design through reaction prediction. In parallel, significant progression in asymmetric organocatalysis has expanded the reach of chiral transfer to new reactions with increased efficiency. However, the complex and diverse catalyst structures applied in this arena have rendered the generalization of asymmetric catalytic processes to be exceptionally challenging. Recognizing this, a portion of our research has been focused on understanding the transferability of chemical observations between similar reactions and exploiting this phenomenon as a platform for prediction. Through these experiences, we have relied on a working knowledge of reaction mechanism to guide the development and application of our models which have been advanced from simple qualitative rules to large statistical models for quantitative predictions. In this feature article, we describe the models acquired to generalize organocatalytic reaction mechanisms and demonstrate their use as a powerful approach for accelerating enantioselective synthesis.

Transferrable selectivity profiles enable prediction in synergistic catalyst space

Organometallic intermediates participate in many multi-catalytic enantioselective transformations directed by a chiral catalyst, but the requirement of optimizing two catalyst components is a significant barrier to widely adopting this approach for chiral molecule synthesis. Algorithms can potentially accelerate the screening process by developing quantitative structure-function relationships from large experimental datasets. However, the chemical data available in this catalyst space is limited. Herein, we report a data-driven strategy that effectively translates selectivity relationships trained on enantioselectivity outcomes derived from one catalyst reaction systems where an abundance of data exists, to synergistic catalyst space. We describe three case studies involving different modes of catalysis (Brønsted acid, chiral anion, and secondary amine) that substantiate the prospect of this approach to predict and elucidate selectivity in reactions where more than one catalyst is involved. Ultimately, the success in applying our approach to diverse areas of asymmetric catalysis implies that this general workflow should find broad use in the study and development of new enantioselective, multi-catalytic processes.

Biomacromolecule-Assisted Screening for Reaction Discovery and Catalyst Optimization

Reaction discovery and catalyst screening lie at the heart of synthetic organic chemistry. While there are efforts at de novo catalyst design using computation/artificial intelligence, at its core, synthetic chemistry is an experimental science. This review overviews biomacromolecule-assisted screening methods and the follow-on elaboration of chemistry so discovered. All three types of biomacromolecules discussed─enzymes, antibodies, and nucleic acids─have been used as "sensors" to provide a readout on product chirality exploiting their native chirality. Enzymatic sensing methods yield both UV-spectrophotometric and visible, colorimetric readouts. Antibody sensors provide direct fluorescent readout upon analyte binding in some cases or provide for cat-ELISA (Enzyme-Linked ImmunoSorbent Assay)-type readouts. DNA biomacromolecule-assisted screening allows for templation to facilitate reaction discovery, driving bimolecular reactions into a pseudo-unimolecular format. In addition, the ability to use DNA-encoded libraries permits the barcoding of reactants. All three types of biomacromolecule-based screens afford high sensitivity and selectivity. Among the chemical transformations discovered by enzymatic screening methods are the first Ni(0)-mediated asymmetric allylic amination and a new thiocyanopalladation/carbocyclization transformation in which both C-SCN and C-C bonds are fashioned sequentially. Cat-ELISA screening has identified new classes of sydnone-alkyne cycloadditions, and DNA-encoded screening has been exploited to uncover interesting oxidative Pd-mediated amido-alkyne/alkene coupling reactions.

A predictive and mechanistic statistical modelling workflow for improving decision making in organic synthesis and catalysis

The application of multivariate linear regression models has been widely utilized as a strategy to streamline the reaction optimization process. While these tools likely provide relatively safe predictions, embedding a method for forecasting the probability of achieving the desired reaction outcome would be valuable for streamlining the identification of promising structures with the best chance of success. Herein, we present a workflow that predicts the probability that a reaction will be successful and is easy and quick to apply. We show that this probabilistic framework can effectively differentiate between predictions often indistinguishable by multivariate linear regression analysis. Moreover, these techniques can enhance the development of mechanistically informative correlations by producing more direct pathways for molecular development and design. Overall, we anticipate this protocol will be generally applicable and useful for accelerating successful chemical discovery.

H-Bonded Counterion-Directed Enantioselective Au(I) Catalysis

A new strategy for enantioselective transition-metal catalysis is presented, wherein a H-bond donor placed on the ligand of a cationic complex allows precise positioning of the chiral counteranion responsible for asymmetric induction. The successful implementation of this paradigm is demonstrated in 5-exo-dig and 6-endo-dig cyclizations of 1,6-enynes, combining an achiral phosphinourea Au(I) chloride complex with a BINOL-derived phosphoramidate Ag(I) salt and thus allowing the first general use of chiral anions in Au(I)-catalyzed reactions of challenging alkyne substrates. Experiments with modified complexes and anions, ¹H NMR titrations, kinetic data, and studies of solvent and nonlinear effects substantiate the key H-bonding interaction at the heart of the catalytic system. This conceptually novel approach, which lies at the intersection of metal catalysis, H-bond organocatalysis, and asymmetric counterion-directed catalysis, provides a blueprint for the development of supramolecularly assembled chiral ligands for metal complexes.

Interrogating the Thionium Hydrogen Bond as a Noncovalent Stereocontrolling Interaction in Chiral Phosphate Catalysis

CH⋯O bonds are a privileged noncovalent interaction determining the energies and geometries of a large number of structures. In catalytic settings, these are invoked as a decisive feature controlling many asymmetric transformations involving aldehydes. However, little is known about their stereochemical role when the interaction involves other substrate types. We report the results of computations that show for the first time thionium hydrogen bonds to be an important noncovalent interaction in asymmetric catalysis. As a validating case study, we explored an asymmetric Pummerer rearrangement involving thionium intermediates to yield enantioenriched N,S-acetals under BINOL-derived chiral phosphate catalysis. DFT and QM/MM hybrid calculations showed that the lowest energy pathway corresponded to a transition state involving two hydrogen bonding interactions from the thionium intermediate to the catalyst. However, the enantiomer resulting from this process differed from the originally published absolute configuration. Experimental determination of the absolute configuration resolved this conflict in favor of our calculations. The reaction features required for enantioselectivity were further interrogated by statistical modeling analysis that utilized bespoke featurization techniques to enable the translation of enantioselectivity trends from intermolecular reactions to those proceeding intramolecularly. Through this suite of computational modeling techniques, a new model is revealed that provides a different explanation for the product outcome and enabled reassignment of the absolute product configuration.

Transferable selectivity profiles allow data from intermolecular reactions using iminium substrates to be applied to predict intramolecular reactions involving thioniums.

Nazarov Cyclizations Catalyzed by BINOL Phosphoric Acid Derivatives: Quantum Chemistry Struggles To Predict the Enantioselectivity

Quantum chemical calculations have successfully predicted the stereoselectivities of many BINOL phosphoric acid catalyzed reactions over the past 10-15 years. Herein we report a contrasting example: a reaction for which standard quantum chemistry techniques have proven unexpectedly ineffective at explaining the stereoselectivity. The Nazarov cyclizations of a divinyl ketone catalyzed by a BINOL phosphoric acid or H₈-BINOL dithiophosphoric acid were studied with a conventional contemporary quantum chemical approach, consisting of transition state optimizations with B3LYP-D3(BJ) and single-point calculations with several functionals in implicit solvent. Unexpectedly, different functionals gave widely different predictions of the level of enantioselectivity and were unable even to agree on which enantiomer of the product would predominate. Molecular dynamics simulations with the OPLS-AA force field provided evidence that the transition state geometries optimized with DFT in the gas phase or in implicit solvent are not good representations of the true transition states of these reactions in solution. One possible reason for this, which may also explain the failure of quantum chemical techniques to reliably predict the enantioselectivity, is the fact that the transition states contain ion pairs which are not highly organized and do not contain any strongly directional noncovalent interactions between the substrate and the catalyst.