Comparing quantitative prediction methods for the discovery of small-molecule chiral catalysts

Organic reactivity from mechanism to machine learning

As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until 'big data' applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barriers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calculations with a quantum-mechanical component are still preferred for quantitative predictions of reactivity. Although big data applications tend to be more qualitative, they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chemical reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mechanical methods, we discuss methods that augment or replace quantum-based modelling with faster alternatives relying on machine learning.

John PC, Kim S, Paton RS. Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. In this Account, we highlight the application and suitability of different representations, from expert-guided "engineered" descriptors to automatically "learned" features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data.The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used.Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as "hand-crafted" molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure-property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors.Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule's weakest bond are used in simple physical models of site-selectivity and reactivity.

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C-N couplings, as well as Pauson-Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model.

Bifunctional squaramides with benzyl-like fragments: analysis of CH⋯π interactions by a multivariate linear regression model and quantum chemical topology

A multivariate linear regression model and quantum chemical topology are used for the quantitative description of non-covalent interactions in the transition state of the Michael addition catalyzed by bifunctional squaramides.

Nickel-catalyzed formation of quaternary carbon centers using tertiary alkyl electrophiles

This review provides a comprehensive summary of recent advances in nickel-catalyzed reactions employing tertiary alkyl electrophiles for the construction of quaternary carbon centers.

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.

Composition- and Condition-Dependent Kinetics of Homogeneous Ester Hydrogenation by a Mn-Based Catalyst

The reaction medium and conditions are the key parameters defining the efficiency and performance of a homogeneous catalyst. In the state-of-the-art molecular descriptions of catalytic systems by density functional theory (DFT) calculations, the reaction medium is commonly reduced to an infinitely diluted ideal solution model. In this work, we carry out a detailed operando computational modeling analysis of the condition dependencies and nonideal solution effects on the mechanism and kinetics of a model ester hydrogenation reaction by a homogeneous Mn(I)-P,N catalyst. By combining DFT calculations, COSMO-RS solvent model, and the microkinetic modeling approach, the kinetic behavior of the multicomponent homogeneous catalyst system under realistic reaction conditions was investigated in detail. The effects of the reaction medium and its dynamic evolution in the course of the reaction were analyzed by comparing the results obtained for the model methyl acetate hydrogenation reaction in a THF solution and under solvent-free neat reaction conditions. The dynamic representations of the reaction medium give rise to strongly nonlinear effects in the kinetic models. The nonideal representation of the reaction medium results in pronounced condition dependencies of the computed energetics of the elementary reaction steps and the computed kinetic profiles but affects only slightly such experimentally accessible kinetic descriptors as the apparent activation energy and the degree of rate control.

Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

The rational design of catalysts remains a challenging endeavor within the broader chemical community owing to the myriad variables that can affect key bond-forming events. Designing selective catalysts for any reaction requires an efficient strategy for discovering predictive structure-activity relationships. Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H-pyrrole (C) via a Ti-catalyzed formal [2 + 2 +1] cycloaddition of phenylpropyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space and k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (>90% C) by incorporating 2,6-dimethyl-4-(pyrrolidin-1-yl)pyridine as a ligand. The origin of catalyst selectivity was probed by examining ISPCA variable loadings in combination with DFT modeling, revealing that ligand lability plays an important role in selectivity. A parallel catalyst search using multivariate linear regression (MLR), a popular approach in catalysis informatics, was also conducted in order to compare these strategies in a hypothetical catalyst scouting campaign. ISPCA appears to be more robust and predictive than MLR when sparse training sets are used that are representative of the data available during the early search for an optimal catalyst. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development.

Asymmetric Catalysis Mediated by Synthetic Peptides, Version 2.0: Expansion of Scope and Mechanisms

Low molecular weight synthetic peptides have been demonstrated to be effective catalysts for an increasingly wide array of asymmetric transformations. In many cases, these peptide-based catalysts have enabled novel multifunctional substrate activation modes and unprecedented selectivity manifolds. These features, along with their ease of preparation, modular and tunable structures, and often biomimetic attributes make peptides well-suited as chiral catalysts and of broad interest. Many examples of peptide-catalyzed asymmetric reactions have appeared in the literature since the last survey of this broad field in Chemical Reviews (Chem. Rev. 2007, 107, 5759-5812). The overarching goal of this new Review is to provide a comprehensive account of the numerous advances in the field. As a corollary to this goal, we survey the many different types of catalytic reactions, ranging from acylation to C-C bond formation, in which peptides have been successfully employed. In so doing, we devote significant discussion to the structural and mechanistic aspects of these reactions that are perhaps specific to peptide-based catalysts and their interactions with substrates and/or reagents.

Mechanistic Studies Inform Design of Improved Ti(salen) Catalysts for Enantioselective [3 + 2] Cycloaddition

Ti(salen) complexes catalyze the asymmetric [3 + 2] cycloaddition of cyclopropyl ketones with alkenes. While high enantioselectivities are achieved with electron-rich alkenes, electron-deficient alkenes are less selective. Herein, we describe mechanistic studies to understand the origins of catalyst and substrate trends in an effort to identify a more general catalyst. Density functional theory (DFT) calculations of the selectivity determining transition state revealed the origin of stereochemical control to be catalyst distortion, which is largely influenced by the chiral backbone and adamantyl groups on the salicylaldehyde moieties. While substitution of the adamantyl groups was detrimental to the enantioselectivity, mechanistic information guided the development of a set of eight new Ti(salen) catalysts with modified diamine backbones. These catalysts were evaluated with four electron-deficient alkenes to develop a three-parameter statistical model relating enantioselectivity to physical organic parameters. This statistical model is capable of quantitative prediction of enantioselectivity with structurally diverse alkenes. These mechanistic insights assisted the discovery of a new Ti(salen) catalyst, which substantially expanded the reaction scope and significantly improved the enantioselectivity of synthetically interesting building blocks.

Connecting and Analyzing Enantioselective Bifunctional Hydrogen Bond Donor Catalysis Using Data Science Tools

The generalization of related asymmetric processes in organocatalyzed reactions is an ongoing challenge due to subtle, noncovalent interactions that drive selectivity. The lack of transferability is often met with a largely empirical approach to optimizing catalyst structure and reaction conditions. This has led to the development of diverse structural catalyst motifs and inspired unique design principles in this field. Bifunctional hydrogen bond donor (HBD) catalysis exemplifies this in which a broad collection of enantioselective transformations has been successfully developed. Herein, we describe the use of data science methods to connect catalyst and substrate structural features of an array of reported enantioselective bifunctional HBD catalysis through an iterative statistical modeling process. The computational parameters used to build the correlations are mechanism-specific based on the proposed transition states, which allows for analysis into the noncovalent interactions responsible for asymmetric induction. The resulting statistical models also allow for extrapolation to out-of-sample examples to provide a prediction platform that can be used for future applications of bifunctional hydrogen bond donor catalysis. Finally, this multireaction workflow presents an opportunity to build statistical models unifying various modes of activation relevant to asymmetric organocatalysis.

Synthetic and computational assessment of a chiral metal-organic framework catalyst for predictive asymmetric transformation

Understanding and controlling molecular recognition mechanisms at a chiral solid interface is a continuously addressed challenge in heterogeneous catalysis. Here, the molecular recognition of a chiral peptide-functionalized metal–organic framework (MOF) catalyst towards a pro-chiral substrate is evaluated experimentally and in silico. The MIL-101 metal–organic framework is used as a macroligand for hosting a Noyori-type chiral ruthenium molecular catalyst, namely (benzene)Ru@MIL-101-NH-Gly-Pro. Its catalytic perfomance toward the asymmetric transfer hydrogenation (ATH) of acetophenone into R- and S-phenylethanol are assessed. The excellent match between the experimentally obtained enantiomeric excesses and the computational outcomes provides a robust atomic-level rationale for the observed product selectivities. The unprecedented role of the MOF in confining the molecular Ru-catalyst and in determining the access of the prochiral substrate to the active site is revealed in terms of highly face-specific host–guest interactions. The predicted surface-specific face differentiation of the prochiral substrate is experimentally corroborated since a three-fold increase in enantiomeric excess is obtained with the heterogeneous MOF-based catalyst when compared to its homogeneous molecular counterpart.

Understanding and controlling molecular recognition mechanisms at a chiral solid interface has been addressed in metal–organic framework catalysts for the asymmetric transfer hydrogenation reaction.

Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes

Molecular details often dictate the macroscopic properties of materials, yet due to their vastly different length scales, relationships between molecular structure and bulk properties can be difficult to predict a priori, requiring Edisonian optimizations and preventing rational design. Here, we introduce an easy-to-execute strategy based on linear free energy relationships (LFERs) that enables quantitative correlation and prediction of how molecular modifications, i.e., substituents, impact the ensemble properties of materials. First, we developed substituent parameters based on inexpensive, DFT-computed energetics of elementary pairwise interactions between a given substituent and other constant components of the material. These substituent parameters were then used as inputs to regression analyses of experimentally measured bulk properties, generating a predictive statistical model. We applied this approach to a widely studied class of electrolyte materials: oligo-ethylene glycol (OEG)-LiTFSI mixtures; the resulting model enables elucidation of fundamental physical principles that govern the properties of these electrolytes and also enables prediction of the properties of novel, improved OEG-LiTFSI-based electrolytes. The framework presented here for using context-specific substituent parameters will potentially enhance the throughput of screening new molecular designs for next-generation energy storage devices and other materials-oriented contexts where classical substituent parameters (e.g., Hammett parameters) may not be available or effective.

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.

Transition State Force Field for the Asymmetric Redox-Relay Heck Reaction

A transition state force field (TSFF) was developed using the quantum-guided molecular mechanics (Q2MM) method to describe the stereodetermining migratory insertion step of the enantioselective redox-relay Heck reaction for a range of multisubstituted alkenes. We show that the TSFF is highly predictive through an external validation of the TSFF against 151 experimentally determined stereoselectivities resulting in an R² of 0.89 and MUE of 1.8 kJ/mol. In addition, limitations in the underlying force field were identified by comparison of the TSFF results to DFT level calculations. A novel application of the TSFF was demonstrated for 31 cases where the enantiomer predicted by the TSFF differed from the originally published values. Experimental determination of the absolute configuration demonstrated that the computational predictions were accurate, suggesting that TSFFs can be used for the rapid prediction of the absolute stereochemistry for a class of reactions. Finally, a virtual ligand screen was conducted utilizing both the TSFF and a simple molecular correlation method. Both methods were similarly predictive, but the TSFF was able to show greater utility through transferability, speed, and interpretability.

Enantioselective Allenoate-Claisen Rearrangement Using Chiral Phosphate Catalysts

Herein we report the first highly enantioselective allenoate-Claisen rearrangement using doubly axially chiral phosphate sodium salts as catalysts. This synthetic method provides access to β-amino acid derivatives with vicinal stereocenters in up to 95% ee. We also investigated the mechanism of enantioinduction by transition state (TS) computations with DFT as well as statistical modeling of the relationship between selectivity and the molecular features of both the catalyst and substrate. The mutual interactions of charge-separated regions in both the zwitterionic intermediate generated by reaction of an amine to the allenoate and the Na⁺-salt of the chiral phosphate leads to an orientation of the TS in the catalytic pocket that maximizes favorable noncovalent interactions. Crucial arene-arene interactions at the periphery of the catalyst lead to a differentiation of the TS diastereomers. These interactions were interrogated using DFT calculations and validated through statistical modeling of parameters describing noncovalent interactions.

Strategies for remote enantiocontrol in chiral gold(iii) complexes applied to catalytic enantioselective γ,δ-Diels-Alder reactions

The use of chiral square planar gold(iii) complexes to access enantioenriched products has rarely been applied in asymmetric catalysis. In this context, we report a mechanistic and synthetic investigation into the use of N-heterocyclic (NHC) gold(iii) complexes in γ,δ-Diels–Alder reactions of 2,4-dienals with cyclopentadiene. The optimal catalyst bearing a unique 2-chloro-1-naphthyl substituent allowed efficient synthesis of functionally rich carbocycles in good yields, diastereo- and enantioselectivities. Transition state and multivariate linear regression (MLR) analysis of both catalyst and substrate trends using molecular descriptors derived from designer parameter acquisition platforms, reveals attractive non-covalent interactions (NCIs) to be key selectivity determinates. These analyses demonstrate that a putative π–π interaction between the substrate proximal double bond and the catalyst aromatic group is an essential feature for high enantioselectivity.

Chiral square planar gold(iii) complexes are employed as catalysts in asymmetric Diels–Alder reactions. The non-covalent interactions responsible for the enantioselectivity are revealed through multivariate linear regression analysis.

A Quantitative Model for Alkane Nucleophilicity Based on C-H Bond Structural/Topological Descriptors

A first quantitative model for calculating the nucleophilicity of alkanes is described. A statistical treatment was applied to the analysis of the reactivity of 29 different alkane C-H bonds towards in situ generated metal carbene electrophiles. The correlation of the recently reported experimental reactivity with two different sets of descriptors comprising a total of 86 parameters was studied, resulting in the quantitative descriptor-based alkane nucleophilicity (QDEAN) model. This model consists of an equation with only six structural/topological descriptors, and reproduces the relative reactivity of the alkane C-H bonds. This reactivity can be calculated from parameters emerging from the schematic drawing of the alkane and a simple set of sums.

Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.

Unveiling the Delicate Balance of Steric and Dispersion Interactions in Organocatalysis Using High-Level Computational Methods

High-level quantum electronic structure calculations are used to provide a deep insight into the mechanism and stereocontrolling factors of two recently developed catalytic asymmetric Diels–Alder (DA) reactions of cinnamate esters with cyclopentadiene. The reactions employ two structurally and electronically very different in situ silylated enantiopure Lewis acid organocatalysts: i.e., binaphthyl-allyl-tetrasulfone (BALT) and imidodiphosphorimidate (IDPi). Each of these catalysts activates only specific substrates in an enantioselective fashion. Emphasis is placed on identifying and quantifying the key noncovalent interactions responsible for the selectivity of these transformations, with the final aim of aiding in the development of designing principles for catalysts with a broader scope. Our results shed light into the mechanism through which the catalyst architecture determines the selectivity of these transformations via a delicate balance of dispersion and steric interactions.

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation

Design of asymmetric catalysts generally involves time- and resource-intensive heuristic endeavors. In view of the steady increase in interest toward efficient catalytic asymmetric reactions and the rapid growth in the field of machine learning (ML) in recent years, we envisaged dovetailing these two important domains. We selected a set of quantum chemically derived molecular descriptors from five different asymmetric binaphthyl-derived catalyst families with the propensity to impact the enantioselectivity of asymmetric hydrogenation of alkenes and imines. The predictive power of the random forest (RF) built using the molecular parameters of a set of 368 substrate-catalyst combinations is found to be impressive, with a root-mean-square error (rmse) in the predicted enantiomeric excess (%ee) of about 8.4 ± 1.8 compared to the experimentally known values. The accuracy of RF is found to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as stepwise linear regression. The proposed method is expected to provide a leap forward in the design of catalysts for asymmetric transformations.

Scaling relationships and volcano plots of homogeneous transition metal catalysis

This Frontier article highlights the recent applications of linear scaling relationships and volcano plots in homogeneous transition metal catalysis.

Predictive Multivariate Linear Regression Analysis Guides Successful Catalytic Enantioselective Minisci Reactions of Diazines

The Minisci reaction is one of the most direct and versatile methods for forging new carbon-carbon bonds onto basic heteroarenes: a broad subset of compounds ubiquitous in medicinal chemistry. While many Minisci-type reactions result in new stereocenters, control of the absolute stereochemistry has proved challenging. An asymmetric variant was recently realized using chiral phosphoric acid catalysis, although in that study the substrates were limited to quinolines and pyridines. Mechanistic uncertainties and nonobvious enantioselectivity trends made the task of extending the reaction to important new substrate classes challenging and time-intensive. Herein, we describe an approach to address this problem through rigorous analysis of the reaction landscape guided by a carefully designed reaction data set and facilitated through multivariate linear regression (MLR) analysis. These techniques permitted the development of mechanistically informative correlations providing the basis to transfer enantioselectivity outcomes to new reaction components, ultimately predicting pyrimidines to be particularly amenable to the protocol. The predictions of enantioselectivity outcomes for these valuable, pharmaceutically relevant motifs were remarkably accurate in most cases and resulted in a comprehensive exploration of scope, significantly expanding the utility and versatility of this methodology. This successful outcome is a powerful demonstration of the benefits of utilizing MLR analysis as a predictive platform for effective and efficient reaction scope exploration across substrate classes.

Site-selective acylation of natural products with BINOL-derived phosphoric acids

The site-selective acylation of a steroidal natural product 19-hydroxydehydroepiandrosterone catalyzed by 1,1'-Bi(2-napthol)-derived (BINOL) chiral phosphoric acids (CPA's) is described. Systematic variation and multivariate linear regression analysis reveal that the same steric parameters typically needed for high enantioselectivity with this class of CPAs are also required for site-selectivity in this case. Density functional theory calculations identify additional weak CH-π interactions as contributors to site discrimination. We further report a rare example of site-selective acylation of phenols through the evaluation of naringenin, a flavonoid natural product, using CPA catalysis. These results suggest that BINOL-derived CPA's may have broader applications in site-selective catalysis.

Reengineering a Reversible Covalent-Bonding Assembly to Optically Detect ee in β-Chiral Primary Alcohols

The use of parallel synthesis protocols for asymmetric reaction discovery has increased the need for new methods to rapidly determine enantiomeric excess (ee) values. Most chirality sensing is performed on stereocenters that are α (i.e., proximal) to the target functional group. Finding a general approach to detect more distant point chirality would increase the substrate scope of such assays. Herein, we demonstrate a design principle to "reach out" to more distant stereocenters, in this case β-chirality in primary alcohols. Therefore, we see the design principles established in this work as a step forward in sensing distant point chirality and, eventually, multi-stereocenter relationships.

Retooling Asymmetric Conjugate Additions for Sterically Demanding Substrates with an Iterative Data-Driven Approach

The development of catalytic enantioselective methods is routinely carried out using easily accessible and prototypical substrates. This approach to reaction development often yields asymmetric methods that perform poorly using substrates that are sterically or electronically dissimilar to those used during the reaction optimization campaign. Consequently, expanding the scope of previously optimized catalytic asymmetric reactions to include more challenging substrates is decidedly nontrivial. Here, we address this challenge through the development of a systematic workflow to broaden the applicability and reliability of asymmetric conjugate additions to substrates conventionally regarded as sterically and electronically demanding. The copper-catalyzed asymmetric conjugate addition of alkylzirconium nucleophiles to form tertiary centers, although successful for linear alkyl chains, fails for more sterically demanding linear α,β-unsaturated ketones. Key to adapting this method to obtain high enantioselectivity was the synthesis of modified phosphoramidite ligands, designed using quantitative structure–selectivity relationships (QSSRs). Iterative rounds of model construction and ligand synthesis were executed in parallel to evaluate the performance of 20 chiral ligands. The copper-catalyzed asymmetric addition is now more broadly applicable, even tolerating linear enones bearing tert-butyl β-substituents. The presence of common functional groups is tolerated in both nucleophiles and electrophiles, giving up to 99% yield and 95% ee across 20 examples.

Holistic prediction of enantioselectivity in asymmetric catalysis

When faced with unfamiliar reaction space, synthetic chemists typically apply reported conditions (reagents, catalyst, solvent, additives) from closely-related reactions to new substrate types. Unfortunately, this approach often fails due to subtle, albeit important, differences in reaction requirements. Consequently, a significant goal in synthetic chemistry is the ability to transfer chemical observations from one reaction to another, quantitatively. Here, we present such a platform by developing a holistic, data-driven workflow for deriving statistical models for one set of reactions that can be applied to predict out-of-sample examples. As a validating case study, published enantioselectivity data sets that employ BINOL-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines were combined and statistical models developed. These models reveal the general interactions imparting asymmetric induction and allow the quantitative transfer of this information to new reaction components. The disclosed techniques create opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development.

After the database of the reactions is constructed, the experimental output, enantiomeric ratios, were mathematically modelled through linear regression techniques to reveal which of the proposed parameters allow for the prediction of new outcomes. The detailed acquisition of parameters can be found in the Supplementary Information and the descriptor tables attached as an accompanying spreadsheet. The models produced were evaluated for their goodness of fit, R², and their robustness is demonstrated by external validations’ goodness of fit, _predR². The nearer the R² and slope values are to one (indicating a tight, one-to-one correlation between predicted and measured outcomes) and the nearer the intercept is to zero (indicating minimal systematic error), the more robust the model. Potential models were refined through number of parameters, because this allows for a mechanistically informative interrogation and cross-validation scores. Leave one reaction out (LORO) analysis was performed to probe general mechanistic principles, which provides the basis for mechanistic transfer of experimental observations and tested further by predicting out-of-sample.

Sustainable Knowledge-Driven Approaches in Transition-Metal-Catalyzed Transformations

The sustainable synthesis of relevant scaffolds for their use in the pharmaceutical, agrochemical, and materials sectors constitutes one of the most urgent challenges that the chemical community needs to overcome. In this context, the development of innovative and more efficient catalytic processes based on a fundamental understanding of the underlying reaction mechanisms remains a largely unresolved challenge for academic and industrial chemists. Herein, selected examples of computational and experimental knowledge-driven approaches for the rational design of transition-metal-catalyzed transformations are discussed.

Disparate Catalytic Scaffolds for Atroposelective Cyclodehydration

Catalysts that control stereochemistry are prized tools in chemical synthesis. When an effective catalyst is found, it is often explored for other types of reactions, frequently under the auspices of different mechanisms. As successes mount, a unique catalyst scaffold may become viewed as "privileged". However, the mechanistic hallmarks of privileged catalysts are not easily enumerated or readily generalized to genuinely different classes of reactions or substrates. We explored the concept of scaffold uniqueness with two catalyst types for an unusual atropisomer-selective cyclodehydration: (a) C2-symmetric chiral phosphoric acids and (b) phosphothreonine-embedded, peptidic phosphoric acids. Pragmatically, both catalyst scaffolds proved fertile for enantioselective/atroposelective cyclodehydrations. Mechanistic studies revealed that the determinants of often equivalent and high atroposelectivity are different for the two catalyst classes. A data-descriptive classification of these asymmetric catalysts reveals an increasingly broad set of catalyst chemotypes, operating with different mechanistic features, that creates new opportunities for broad and complementary application of catalyst scaffolds in diverse substrate space.