Comprehensive Stereochemical Models for Selectivity Prediction in Diverse Chiral Phosphate-Catalyzed Reaction Space

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.

Insights into Synergistic Effects of Counterion and Ligand on Diastereoselectivity Switch in Gold-Catalyzed Post-Ugi Ipso-Cyclization

The concept of diastereoselectivity switch in gold catalysis is investigated, which primarily depends on the effects of ligand and counterion. The origins of gold-catalyzed post-Ugi ipso-cyclization for the diastereoselective synthesis of spirocyclic pyrrol-2-one-dienone have been explored with density functional theory calculations. The reported mechanism emphasized the importance of the cooperation of ligand and counterion in diastereoselectivity switch, leading to the stereocontrolling transition states. Furthermore, the nonbonding interactions primarily between the catalyst and the substrate play a significant role in the cooperation of ligand and counterion. This work would be useful to further understand the reaction mechanism of gold-catalyzed cyclization and the effects of ligand and counterion.

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.

OSCAR: an extensive repository of chemically and functionally diverse organocatalysts

The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of 4000 experimentally derived organocatalysts along with their corresponding building blocks and combinatorially enriched structures. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available (https://archive.materialscloud.org/record/2022.106) and constitute the starting point to build generative and predictive models for organocatalyst performance.

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.

Guiding Target Synthesis with Statistical Modeling Tools: A Case Study in Organocatalysis

Practitioners are generally not willing to explore modern reactions where considerable synthetic effort is required to generate materials and the results are not certain. Organocatalysis exemplifies this, in which a broad set of enantioselective reactions have been successfully developed but further applications to include additional substrates are often not performed. Herein we demonstrate how statistical models can be utilized to accurately distinguish between different catalysts and reactions to guide the selection of efficient synthetic routes to obtain a target molecule.

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.