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

Controlling Stereoselectivity with Noncovalent Interactions in Chiral Phosphoric Acid Organocatalysis

Chiral phosphoric acids (CPAs) have emerged as highly effective Brønsted acid catalysts in an expanding range of asymmetric transformations, often through novel multifunctional substrate activation modes. Versatile and broadly appealing, these catalysts benefit from modular and tunable structures, and compatibility with additives. Given the unique types of noncovalent interactions (NCIs) that can be established between CPAs and various reactants─such as hydrogen bonding, aromatic interactions, and van der Waals forces─it is unsurprising that these catalyst systems have become a promising approach for accessing diverse chiral product outcomes. This review aims to provide an in-depth exploration of the mechanisms by which CPAs impart stereoselectivity, positioning NCIs as the central feature that connects a broad spectrum of catalytic reactions. Spanning literature from 2004 to 2024, it covers nucleophilic additions, radical transformations, and atroposelective bond formations, highlighting the applicability of CPA organocatalysis. Special emphasis is placed on the structural and mechanistic features that govern CPA-substrate interactions, as well as the tools and techniques developed to enhance our understanding of their catalytic behavior. In addition to emphasizing mechanistic details and stereocontrolling elements in individual reactions, we have carefully structured this review to provide a natural progression from these specifics to a broader, class-level perspective. Overall, these findings underscore the critical role of NCIs in CPA catalysis and their significant contributions to advancing asymmetric synthesis.

Connecting the complexity of stereoselective synthesis to the evolution of predictive tools

Synthetic methods have seemingly progressed to an extent where there is an apparent and increasing need for predictive models to navigate the vast chemical space. Methods for anticipating and optimizing reaction outcomes have evolved from simple qualitative pictures generated from chemical intuition to complex models constructed from quantitative methods like quantum chemistry and machine learning. These toolsets are rooted in physical organic chemistry where fundamental principles of chemical reactivity and molecular interactions guide their development and application. Here, we detail how the evolution of these methods is a successful outcome and a powerful response to the diverse synthetic challenges confronted and the innovative selectivity concepts introduced. In this review, we perform a periodization of organic chemistry focusing on strategies that have been applied to guide the synthesis of chiral organic molecules.

Total synthesis of 25 picrotoxanes by virtual library selection

The synthesis of a complex molecule begins from an initial design stage1-4 in which possible routes are triaged by strategy and feasibility, on the basis of analogy to similar reactions2,3. However, as molecular complexity increases, predictability decreases5; inevitably, even experienced chemists resort to trial and error to identify viable intermediates en route to the target molecule. We encountered such a problem in the synthesis of picrotoxane sesquiterpenes in which pattern-recognition methods anticipated success, but small variations in structure led to failure. Here, to solve this problem but avoid tedious guess-and-check experimentation, we built a virtual library of elusive late-stage intermediate analogues that were triaged by reactivity and altered the synthesis pathway. The efficiency of this method led to concise routes to 25 naturally occurring picrotoxanes. Costly density-functional-theory transition-state calculations were replaced with faster reactant parameterizations to increase scalability and, in this case, inform the mechanism. This approach can serve as an add-on search to human or computer-assisted synthesis planning applicable to high-complexity targets and/or steps with little representation in the literature or reaction databases.

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.

Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often nontransferable and cannot be applied across different reactions. To overcome this drawback, we introduce a general featurization strategy for bidentate ligands that is coupled with an automated feature selection pipeline and Bayesian ridge regression to perform multivariate linear regression modeling. This approach, which is applicable to any reaction, incorporates electronic, steric, and topological features (rigidity/flexibility, branching, geometry, and constitution) and is well-suited for early stage ligand optimization. Using only small data sets, our workflow capably predicts the enantioselectivity of four metal-catalyzed asymmetric reactions. Uncertainty estimates provided by Bayesian ridge regression permit the use of Bayesian optimization to efficiently explore pools of prospective ligands. Finally, we constructed the BDL-Cu-2023 data set, composed of 312 bidentate ligands extracted from the Cambridge Structural Database, and screened it with this procedure to identify ligand candidates for a challenging asymmetric oxy-alkynylation reaction.

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.

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.

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.

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.

Harvesting the fragment-based nature of bifunctional organocatalysts to enhance their activity

Enhancing the activity of bifunctional organocatalysts: a fragment-based approach coupled with activity maps helps identifying better-performing catalytic motifs.