The Development of Multidimensional Analysis Tools for Asymmetric Catalysis and Beyond

When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?

Deep graph neural networks are extensively utilized to predict chemical reactivity and molecular properties. However, because of the complexity of chemical space, such models often have difficulty extrapolating beyond the chemistry contained in the training set. Augmenting the model with quantum mechanical (QM) descriptors is anticipated to improve its generalizability. However, obtaining QM descriptors often requires CPU-intensive computational chemistry calculations. To identify when QM descriptors help graph neural networks predict chemical properties, we conduct a systematic investigation of the impact of atom, bond, and molecular QM descriptors on the performance of directed message passing neural networks (D-MPNNs) for predicting 16 molecular properties. The analysis surveys computational and experimental targets, as well as classification and regression tasks, and varied data set sizes from several hundred to hundreds of thousands of data points. Our results indicate that QM descriptors are mostly beneficial for D-MPNN performance on small data sets, provided that the descriptors correlate well with the targets and can be readily computed with high accuracy. Otherwise, using QM descriptors can add cost without benefit or even introduce unwanted noise that can degrade model performance. Strategic integration of QM descriptors with D-MPNN unlocks potential for physics-informed, data-efficient modeling with some interpretability that can streamline de novo drug and material designs. To facilitate the use of QM descriptors in machine learning workflows for chemistry, we provide a set of guidelines regarding when and how to best leverage QM descriptors, a high-throughput workflow to compute them, and an enhancement to Chemprop, a widely adopted open-source D-MPNN implementation for chemical property prediction.

Automation and machine learning augmented by large language models in a catalysis study

Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.

High Resolution Ion Mobility Enables the Structural Characterization of Atropisomers of GDC-6036, a KRAS G12C Covalent Inhibitor

GDC-6036 is a covalent KRAS G12C inhibitor that demonstrates high potency and selectivity. Structurally, GDC-6036 consists of several motifs that make the analytical characterization of this molecule challenging, including a highly basic pyrrolidine motif bonded to a quinazoline ring via an ether bond and an atropisomeric carbon-carbon bond between functionalized pyridine and quinazoline groups. Structurally, the desired atropisomer was synthesized via an atroposelective Negishi coupling with very high yield. However, having a direct way to analyze and confirm the presence of the atropisomeric species remained challenging in routine analytical workflows. In this study, both variable temperature nuclear magnetic resonance (VT-NMR) and two different approaches of in-line ion mobility coupled to liquid chromatography mass spectrometry (LC-MS) workflows were evaluated for the characterization of GDC-6036 and its undesired atropisomer (Compound B) to support synthetic route development. Briefly, both VT-NMR and traveling wave ion mobility spectrometry (TWIMS) enabled by structures for lossless ion manipulation (SLIM) technology coupled to high resolution MS (HRMS) are able to elucidate the structures of the atropisomers in a complex mixture. Drift tube IMS (DTIMS) was also evaluated, but lacked the resolving power to demonstrate separation between the two species in a mixture, but did show slight differences in their arrival times when multiplexed and injected separately. The determined resolving power (Rp) by multiplexing the ions via DTIMS was 67.3 and 60.5 for GDC-6036 and Compound B, respectively, while the two peak resolving power (Rpp) was determined to be 0.41, indicating inadequate resolution between the two species. Alternatively, the SLIM-IM studies showed Rp of 103.8 and 99.4, with a Rpp of 2.64, indicating good separation between the atropisomers. Furthermore, the CCS/z for GDC-6036 and Compound B was determined to be 231.2 Å2/z and 235.0 Å2/z, respectively. Quantitative experiments demonstrate linearity (R2 >0.99) for both GDC-6036 and Compound B while maintaining separation via SLIM-IM. Spike recoveries of one atropisomer relative to the other yielded strong recoveries (98.7% to 102.5%) while maintaining reproducibility (<7% RSD). The study herein describes the analytical process for evaluating new technologies and strategies for implementation in routine biopharmaceutical characterization workflows.

Data-science-guided calibration curve prediction of an MLCT-based ee determination assay for chiral amines

Circular dichroism (CD) based enantiomeric excess (ee) determination assays are optical alternatives to chromatographic ee determination in high-throughput screening (HTS) applications. However, the implementation of these assays requires calibration experiments using enantioenriched materials. We present a data-driven approach that circumvents the need for chiral resolution and calibration experiments for an octahedral Fe(II) complex (1) used for the ee determination of α-chiral primary amines. By computationally parameterizing the imine ligands formed in the assay conditions, a model of the circular dichroism (CD) response of the Fe(II) assembly was developed. Using this model, calibration curves were generated for four analytes and compared to experimentally generated curves. In a single-blind ee determination study, the ee values of unknown samples were determined within 9% mean absolute error, which rivals the error using experimentally generated calibration curves.

Bayesian optimization of glycopolymer structures for the interaction with cholera toxin B subunit

The optimal structure of synthetic glycopolymers for GM1 mimetics was determined through Bayesian optimization. The interactions of glycopolymers carrying galactose and neuraminic acid units in different compositions with cholera toxin B subunit (CTB) were assessed by an enzyme-linked immunosorbent assay (ELISA). Gaussian process regression, using the ELISA results, predicted the composition of glycopolymers that would exhibit stronger interactions with CTB. Following five cycles of optimization, the glycopolymers carrying 60 mol% galactose and 25 mol% neuraminic acid demonstrated an IC50 value of 75 μM for CTB, representing the lowest value among the synthesized glycopolymers.

Paving the road towards automated homogeneous catalyst design

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

Tricyclononenes and tricyclononadienes as efficient monomers for controlled ROMP: understanding structure-propagation rate relationships and enabling facile post-polymerization modification

Grubbs 3rd-generation (G3) pre-catalyst-initiated ring-opening metathesis polymerization (ROMP) remains an indispensable tool in the polymer chemist's toolbox. Tricyclononenes (TCN) and tricyclononadienes (TCND) represent under-explored classes of monomers for ROMP that have the potential to both advance fundamental knowledge (e.g., structure-polymerization kinetics relationships) and serve as practical tools for the polymer chemist (e.g., post-polymerization functionalization). In this work, a library of TCN and TCND imides, monoesters, and diesters, along with their exo-norbornene counterparts, were synthesized to compare their behaviors in G3-initiated ROMP. Real-time 1H NMR was used to study their polymerization kinetics; propagation rates (k p) were extracted for each monomer. To understand the relationships between monomer structure and ROMP propagation rates, density functional theory methods were used to calculate a variety of electronic and steric parameters for each monomer. While electronic parameters (e.g., HOMO energy levels) correlated positively with the measured k p values, steric parameters generally gave improved correlations, which indicates that monomer size and shape are better predictors for k p than electronic parameters for this data set. Furthermore, the TCND diester-which contains an electron-deficient cyclobutene that is resistant to ROMP-and its polymer p(TCND) are shown to be highly reactive toward DBU-catalyzed conjugate addition reactions with thiols, providing a protecting- and activating-group free strategy for post-polymerization modification.

A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target

A catalyst possessing a broad substrate scope, in terms of both turnover and enantioselectivity, is sometimes called "general". Despite their great utility in asymmetric synthesis, truly general catalysts are difficult or expensive to discover via traditional high-throughput screening and are, therefore, rare. Existing computational tools accelerate the evaluation of reaction conditions from a pre-defined set of experiments to identify the most general ones, but cannot generate entirely new catalysts with enhanced substrate breadth. For these reasons, we report an inverse design strategy based on the open-source genetic algorithm NaviCatGA and on the OSCAR database of organocatalysts to simultaneously probe the catalyst and substrate scope and optimize generality as a primary target. We apply this strategy to the Pictet-Spengler condensation, for which we curate a database of 820 reactions, used to train statistical models of selectivity and activity. Starting from OSCAR, we define a combinatorial space of millions of catalyst possibilities, and perform evolutionary experiments on a diverse substrate scope that is representative of the whole chemical space of tetrahydro-β-carboline products. While privileged catalysts emerge, we show how genetic optimization can address the broader question of generality in asymmetric synthesis, extracting structure-performance relationships from the challenging areas of chemical space.

Enantioselective C(sp2)-C(sp3) Bond Construction by Ni Catalysis

ConspectusAfter decades of palladium dominating the realm of transition-metal-catalyzed cross-coupling, recent years have witnessed exciting advances in the development of new nickel-catalyzed cross-coupling reactions to form C(sp3) centers. Nickel possesses distinct properties compared with palladium, such as facile single-electron transfer to C(sp3) electrophiles and rapid C-C reductive elimination from NiIII. These properties, among others, make nickel particularly well-suited for reductive cross-coupling (RCC) in which two electrophiles are coupled and an exogenous reductant is used to turn over the metal catalyst. Ni-catalyzed RCCs use readily available and stable electrophiles as starting materials and exhibit good functional group tolerance, which makes them appealing for applications in the synthesis of complex molecules. Building upon the foundational work in Ni-catalyzed RCCs by the groups of Kumada, Durandetti, Weix, and others, as well as the advancements in Ni-catalyzed enantioselective redox-neutral cross-couplings led by Fu and co-workers, we initiated a program to explore the feasibility of developing highly enantioselective Ni-catalyzed RCCs. Our research has also been driven by a keen interest in unraveling the factors contributing to enantioinduction and electrophile activation as we seek new avenues for advancing our understanding and further developing these reactions.In the first part of this Account, we organize our reported methods on the basis of the identity of the C(sp3) electrophiles, including benzylic chlorides, N-hydroxyphthalimide (NHP) esters, and α-chloro esters and nitriles. We highlight how the selection of specific chiral ligands plays a pivotal role in achieving high cross-selectivity and enantioselectivity. In addition, we show that reduction can be accomplished not only with heterogeneous reductants, such as Mn0, but also with the soluble organic reductant tetrakis(dimethylamino)ethylene (TDAE), as well as electrochemically. The use of homogeneous reductants, such as TDAE, is well suited for studying the mechanism of the transformation. Although this Account primarily focuses on RCCs, we also highlight our work using trifluoroborate (BF3K) salts as radical precursors for enantioselective dual-Ni/photoredox systems.At the end of this Account, we summarize the relevant mechanistic studies of two closely related asymmetric reductive alkenylation reactions developed in our laboratory and provide a context between our work and related mechanistic studies by others. We discuss how the ligand properties influence the rates and mechanisms of electrophile activation and how understanding the mode of C(sp3) radical generation can be used to optimize the yield of an RCC. Our research endeavors to offer insights on the intricate mechanisms at play in asymmetric Ni-catalyzed RCCs with the goal of using the rate of electrophile activation to improve the substrate scope of enantioselective RCCs. We anticipate that the insights we share in this Account can provide guidance for the development of new methods in this field.

Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis

Automated chemistry platforms have been widely explored, but many focus on fixed tasks for chemical synthesis or analysis. However, a typical synthetic chemistry workflow utilizes both, such as kinetic measurements for reaction development and optimization. Due to their repetitive and time-consuming nature, kinetic measurements are often omitted, which limits the mechanistic investigation of reactions. Herein, we present a "Chemputer" platform with on-line analytics (UV/Vis, NMR) which automates routine kinetic measurements. The system's capabilities are showcased by exploring an inverse electron-demand Diels-Alder using initial rate measurements, a metal complexation using variable time normalization analysis (VTNA), and formation of a series of tosylamide derivatives using Hammett analysis. Over 60 individual experiments are presented which required minimal intervention, highlighting the significant time savings of automation. Owing to the modular design of the platform, which facilitates rapid integration of commercial analytical tools, our approach is widely accessible and adjustable to the reaction under investigation. The platform is operated using the chemical programming language, XDL, hence experimental procedures and results are stored in a precise, computer-readable format. We propose that widespread adoption of this reporting protocol in the chemical community could build a database of validated kinetic data beneficial for Machine Learning.

%VBur index and steric maps: from predictive catalysis to machine learning

Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups of atoms in molecules. They are important in determining the reactivity, stability, and physical properties of chemical compounds. One commonly used steric index is the steric hindrance, which refers to the obstruction or hindrance of movement in a molecule caused by bulky substituents or functional groups. Steric hindrance can affect the reactivity of a molecule by altering the accessibility of its reactive sites and influencing the geometry of its transition states. Notably, the Tolman cone angle and %VBur are prominent among these indices. Actually, steric effects can also be described using the concept of steric bulk, which refers to the space occupied by a molecule or functional group. Steric bulk can affect the solubility, melting point, boiling point, and viscosity of a substance. Even though electronic indices are more widely used, they have certain drawbacks that might shift preferences towards others. They present a higher computational cost, and often, the weight of electronics in correlation with chemical properties, e.g. binding energies, falls short in comparison to %VBur. However, it is worth noting that this may be because the steric index inherently captures part of the electronic content. Overall, steric indices play an important role in understanding the behaviour of chemical compounds and can be used to predict their reactivity, stability, and physical properties. Predictive chemistry is an approach to chemical research that uses computational methods to anticipate the properties and behaviour of these compounds and reactions, facilitating the design of new compounds and reactivities. Within this domain, predictive catalysis specifically targets the prediction of the performance and behaviour of catalysts. Ultimately, the goal is to identify new catalysts with optimal properties, leading to chemical processes that are both more efficient and sustainable. In this framework, %VBur can be a key metric for deepening our understanding of catalysis, emphasizing predictive catalysis and sustainability. Those latter concepts are needed to direct our efforts toward identifying the optimal catalyst for any reaction, minimizing waste, and reducing experimental efforts while maximizing the efficacy of the computational methods.

Automated de Novo Design of Olefin Metathesis Catalysts: Computational and Experimental Analysis of a Simple Thermodynamic Design Criterion

Methods for computational de novo design of inorganic molecules have paved the way for automated design of homogeneous catalysts. Such studies have so far relied on correlation-based prediction models as fitness functions (figures of merit), but the soundness of these approaches has yet to be tested by experimental verification of de novo-designed catalysts. Here, a previously developed criterion for the optimization of dative ligands L in ruthenium-based olefin metathesis catalysts RuCl2(L)(L')(═CHAr), where Ar is an aryl group and L' is a phosphine ligand dissociating to activate the catalyst, was used in de novo design experiments. These experiments predicted catalysts bearing an N-heterocyclic carbene (L = 9) substituted by two N-bound mesityls and two tert-butyl groups at the imidazolidin-2-ylidene backbone to be promising. Whereas the phosphine-stabilized precursor assumed by the prediction model could not be made, a pyridine-stabilized ruthenium alkylidene complex (17) bearing carbene 9 was less active than a known leading pyridine-stabilized Grubbs-type catalyst (18, L = H2IMes). A density functional theory-based analysis showed that the unsubstituted metallacyclobutane (MCB) intermediate generated in the presence of ethylene is the likely resting state of both 17 and 18. Whereas the design criterion via its correlation between the stability of the MCB and the rate-determining barrier indeed seeks to stabilize the MCB, it relies on RuCl2(L)(L')(═CH2) adducts as resting states. The change in resting state explains the discrepancy between the prediction and the actual performance of catalyst 17. To avoid such discrepancies and better address the multifaceted challenges of predicting catalytic performance, future de novo catalyst design studies should explore and test design criteria incorporating information from more than a single relative energy or intermediate.

Deconvoluting Nonlinear Catalyst-Substrate Effects in the Intramolecular Dirhodium-Catalyzed C-H Insertion of Donor/Donor Carbenes Using Data Science Tools

Interactions between catalysts and substrates can be highly complex and dynamic, often complicating the development of models to either predict or understand such processes. A dirhodium(II)-catalyzed C-H insertion of donor/donor carbenes into 2-alkoxybenzophenone substrates to form benzodihydrofurans was selected as a model system to explore nonlinear methods to achieve a mechanistic understanding. We found that the application of traditional methods of multivariate linear regression (MLR) correlating DFT-derived descriptors of catalysts and substrates leads to poorly performing models. This inspired the introduction of nonlinear descriptor relationships into modeling by applying the sure independence screening and sparsifying operator (SISSO) algorithm. Based on SISSO-generated descriptors, a high-performing MLR model was identified that predicts external validation points well. Mechanistic interpretation was aided by the deconstruction of feature relationships using chemical space maps, decision trees, and linear descriptors. Substrates were found to have a strong dependence on steric effects for determining their innate cyclization selectivity preferences. Catalyst reactive site features can then be matched to product features to tune or override the resultant diastereoselectivity within the substrate-dictated ranges. This case study presents a method for understanding complex interactions often encountered in catalysis by using nonlinear modeling methods and linear deconvolution by pattern recognition.

Dataset Design for Building Models of Chemical Reactivity

Models can codify our understanding of chemical reactivity and serve a useful purpose in the development of new synthetic processes via, for example, evaluating hypothetical reaction conditions or in silico substrate tolerance. Perhaps the most determining factor is the composition of the training data and whether it is sufficient to train a model that can make accurate predictions over the full domain of interest. Here, we discuss the design of reaction datasets in ways that are conducive to data-driven modeling, emphasizing the idea that training set diversity and model generalizability rely on the choice of molecular or reaction representation. We additionally discuss the experimental constraints associated with generating common types of chemistry datasets and how these considerations should influence dataset design and model building.

Kinetically-driven reactivity of sulfinylamines enables direct conversion of carboxylic acids to sulfinamides

Sulfinamides are some of the most centrally important four-valent sulfur compounds that serve as critical entry points to an array of emergent medicinal functional groups, molecular tools for bioconjugation, and synthetic intermediates including sulfoximines, sulfonimidamides, and sulfonimidoyl halides, as well as a wide range of other S(iv) and S(vi) functionalities. Yet, the accessible chemical space of sulfinamides remains limited, and the approaches to sulfinamides are largely confined to two-electron nucleophilic substitution reactions. We report herein a direct radical-mediated decarboxylative sulfinamidation that for the first time enables access to sulfinamides from the broad and structurally diverse chemical space of carboxylic acids. Our studies show that the formation of sulfinamides prevails despite the inherent thermodynamic preference for the radical addition to the nitrogen atom, while a machine learning-derived model facilitates prediction of the reaction efficiency based on computationally generated descriptors of the underlying radical reactivity.

Unusual Kinetics Induced by Ligands in Rhodium(III)-Catalyzed Dehydrogenative Olefination Reactions

In this study, we investigate the effects of ligands on C-H activation during rhodium(III)-catalyzed C-H bond olefination reactions using well-defined [CpXRhIII] catalytic systems with three representative CpX (Cp (η5-C5H5), CpCF3 (η5-C5Me4CF3), and Cp* (η5-C5Me5)) ligands. Our results demonstrate that C-H activation as the rate-limiting step is significantly influenced by the steric properties of the CpX ligands. Moreover, we observe a dramatic acceleration of the simple [CpRhIII]-catalyzed C-H olefination reaction with acid coproducts such as HOAc, implying an autocatalytic C-H activation process.

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery

Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules; in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field.

An Asymmetric Aromatic Finkelstein Reaction: A Platform for Remote Diarylmethane Desymmetrization

A first-of-its-kind enantioselective aromatic Finkelstein reaction is disclosed for the remote desymmetrization of diarylmethanes. The reaction operates through a copper-catalyzed C-I bond-forming event, and high levels of enantioselectivity are achieved through the deployment of a tailored guanidinylated peptide ligand. Strategic use of transition-metal-mediated reactions enables the chemoselective modification of the aryl iodide products; thus, the synthesis of a diverse set of otherwise difficult-to-access diarylmethanes with excellent levels of selectivity is realized from a common intermediate. A mixed experimental/computational analysis of steric parameters and substrate conformations identifies the importance of remote conformational effects as a key to achieving high enantioselectivity in this desymmetrization reaction.

Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions

Burgeoning developments in machine learning (ML) and its rapidly growing adaptations in chemistry are noteworthy. Motivated by the successful deployments of ML in the realm of molecular property prediction (MPP) and chemical reaction prediction (CRP), herein we highlight some of its most recent applications in predictive chemistry. We present a nonmathematical and concise overview of the progression of ML implementations, ranging from an ensemble-based random forest model to advanced graph neural network algorithms. Similarly, the prospects of various feature engineering and feature learning approaches that work in conjunction with ML models are described. Highly accurate predictions reported in MPP tasks (e.g., lipophilicity, solubility, distribution coefficient), using methods such as D-MPNN, MolCLR, SMILES-BERT, and MolBERT, offer promising avenues in molecular design and drug discovery. Whereas MPP pertains to a given molecule, ML applications in chemical reactions present a different level of challenge, primarily arising from the simultaneous involvement of multiple molecules and their diverse roles in a reaction setting. The reported RMSEs in MPP tasks range from 0.287 to 2.20, while those for yield predictions are well over 4.9 in the lower end, reaching thresholds of >10.0 in several examples. Our Review concludes with a set of persisting challenges in dealing with reaction data sets and an overall optimistic outlook on benefits of ML-driven workflows for various MPP as well as CRP tasks.

Reactivity Prediction of Cu-Catalyzed Halogen Atom Transfer Reactions Using Data-Driven Techniques

In catalysis, linear free energy relationships (LFERs) are commonly used to identify reaction descriptors that enable the prediction of outcomes and the design of more effective catalysts. Herein, LFERs are established for the reductive cleavage of the C(sp3)-X bond in alkyl halides (RX) by Cu complexes. This reaction represents the activation step in atom transfer radical polymerization and atom transfer radical addition/cyclization. The values of the activation rate constant, kact, for 107 Cu complex/RX couples in 5 different solvents spanning over 13 orders of magnitude were effectively interpolated by the equation: log kact = sC(I + C + S), where I, C, and S are, respectively, the initiator, catalyst, and solvent parameters, and sC is the catalyst-specific sensitivity parameter. Furthermore, each of these parameters was correlated to relevant descriptors, which included the bond dissociation free energy of RX and its Tolman cone angle θ, the electron affinity of X, the radical stabilization energy, the standard reduction potential of the Cu complex, the polarizability parameter π* of the solvent, and the distortion energy of the complex in its transition state. This set of descriptors establishes the fundamental properties of Cu complexes and RX that determine their reactivity and that need to be considered when designing novel systems for atom transfer radical reactions. Finally, a multivariate linear regression (MLR) approach was adopted to develop an objective model that surpassed the predictive capability of the LFER equation. Thus, the MLR model was employed to predict kact values for >2000 Cu complex/RX pairs.

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.

Using Data Science for Mechanistic Insights and Selectivity Predictions in a Non-Natural Biocatalytic Reaction

The study of non-natural biocatalytic transformations relies heavily on empirical methods, such as directed evolution, for identifying improved variants. Although exceptionally effective, this approach provides limited insight into the molecular mechanisms behind the transformations and necessitates multiple protein engineering campaigns for new reactants. To address this limitation, we disclose a strategy to explore the biocatalytic reaction space and garner insight into the molecular mechanisms driving enzymatic transformations. Specifically, we explored the selectivity of an "ene"-reductase, GluER-T36A, to create a data-driven toolset that explores reaction space and rationalizes the observed and predicted selectivities of substrate/mutant combinations. The resultant statistical models related structural features of the enzyme and substrate to selectivity and were used to effectively predict selectivity in reactions with out-of-sample substrates and mutants. Our approach provided a deeper understanding of enantioinduction by GluER-T36A and holds the potential to enhance the virtual screening of enzyme mutants.

A substrate descriptor based approach for the prediction and understanding of the regioselectivity in caged catalyzed hydroformylation

The use of data driven tools to predict the selectivity of homogeneous catalysts has received considerable attention in the past years. In these studies often the catalyst structure is varied, but the use of substrate descriptors to rationalize the catalytic outcome is relatively unexplored. To study whether this may be an effective tool, we investigated both an encapsulated and a non-encapsulated rhodium based catalyst in the hydroformylation reaction of 41 terminal alkenes. For the non-encapsulated catalyst, CAT2, the regioselectivity of the acquired substrate scope could be predicted with high accuracy using the Δ13C NMR shift of the alkene carbon atoms as a descriptor (R2 = 0.74) and when combined with a computed intensity of the CC stretch vibration (ICC stretch) the accuracy increased further (R2 = 0.86). In contrast, a substrate descriptor approach with an encapsulated catalyst, CAT1, appeared more challenging indicating a confined space effect. We investigated Sterimol parameters of the substrates as well as computer-aided drug design descriptors of the substrates, but these parameters did not result in a predictive formula. The most accurate substrate descriptor based prediction was made with the Δ13C NMR shift and ICC stretch (R2 = 0.52), suggestive of the involvement of CH-π interactions. To further understand the confined space effect of CAT1, we focused on the subset of 21 allylbenzene derivatives to investigate predictive parameters unique for this subset. These results showed the inclusion of a charge parameter of the aryl ring improved the regioselectivity predictions, which is in agreement with our assessment that noncovalent interactions between the phenyl ring of the cage and the aryl ring of the substrate are relevant for the regioselectivity outcome. However, the correlation is still weak (R2 = 0.36) and as such we are investigating novel parameters that should improve the overall regioselectivity outcome.

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.

Feature Analysis in High-Dimensional Data: Structure-Activity Relationships of Lewis Acid-Transition-Metal Complex-Catalyzed H2 Activation

Lewis acid-transition metal (LA-TM) catalysts have been proven to have an advantage in catalyzing hydrogen activation. Herein, a high-dimensional structure-activity relationship study is performed for LA-TM-catalyzed hydrogen activation by density functional theory calculations. The DPB-Ni complex is taken as the representative catalyst, and the explored Lewis acid sites and transition-metal centers include B, Al, Ga and Ni, Pd, Pt, respectively. Totally, four general hydrogen activation mechanisms are systematically studied among the nine catalytic systems. The Ga-Ni system undergoes the lowest free energy of activation (11.0 kcal/mol), which is considered to be the optimal combination of the Lewis acid site and transition-metal center. Furthermore, more than 100 parameters are used to analyze the structure-activity relationship, including the physical structure, the bond order, the atom charge, and many other properties. Key parameters of important structures are dug out to show a high correlation with the activity of the LA-TM systems, including the M-H₂ distance, the H-H bond length, the second-order perturbation stabilization energy of M-H₂, the bond order of the LA-TM, and so on. The multivariable analysis indicates that the feature related to the basic elemental properties and the global feature codetermine the activity of the catalyst. In the LA-TM system, the combination of Ip_LA/Ip_TM (Ip, the first ionization energy, the feature related to basic elemental properties) and the chemical hardness (the global feature) can better explain the activity of the catalyst. The Ip_LA/Ip_TM reflects the difficulty of breaking the LA-TM bond, affecting the reaction site of activating hydrogen. The hardness reflects the stability and reactivity of LA-TM-RC complexes. The above two features with the addition of the LA-TM bond length (the local feature) can better reflect the activity of the LA-TM system-catalyzed H₂ activation. The feature combinations and the method of multidimensional data analysis should be informative guidance for the rational design of efficient LA-TM catalysts for H₂ activation.

Versatile Triphenylphosphine-Containing Polymeric Catalysts and Elucidation of Structure-Function Relationships

Synthetic polymers are a modular solution to bridging the two most common classes of catalysts: proteins and small molecules. Polymers offer the synthetic versatility of small-molecule catalysts while simultaneously having the ability to construct microenvironments mimicking those of natural proteins. We synthesized a panel of polymeric catalysts containing a novel triphenylphosphine acrylamide monomer and investigated how their properties impact the rate of a model Suzuki-Miyaura cross-coupling reaction. Systematic variation of polymer properties, such as the molecular weight, functional density, and comonomer identity, led to tunable reaction rates and solvent compatibility, including full conversion in an aqueous medium. Studies with bulkier substrates revealed connections between polymer parameters and reaction conditions that were further elucidated with a regression analysis. Some connections were substrate-specific, highlighting the value of the rapidly tunable polymer catalyst. Collectively, these results aid in building structure-function relationships to guide the development of polymer catalysts with tunable substrates and environmental compatibility.

Interrogating the Mechanistic Features of Ni(I)-Mediated Aryl Iodide Oxidative Addition Using Electroanalytical and Statistical Modeling Techniques

While the oxidative addition of Ni(I) to aryl iodides has been commonly proposed in catalytic methods, an in-depth mechanistic understanding of this fundamental process is still lacking. Herein, we describe a detailed mechanistic study of the oxidative addition process using electroanalytical and statistical modeling techniques. Electroanalytical techniques allowed rapid measurement of the oxidative addition rates for a diverse set of aryl iodide substrates and four classes of catalytically relevant complexes (Ni(MeBPy), Ni(MePhen), Ni(Terpy), and Ni(BPP)). With >200 experimental rate measurements, we were able to identify essential electronic and steric factors impacting the rate of oxidative addition through multivariate linear regression models. This has led to a classification of oxidative addition mechanisms, either through a three-center concerted or halogen-atom abstraction pathway based on the ligand type. A global heat map of predicted oxidative addition rates was created and shown applicable to a better understanding of the reaction outcome in a case study of a Ni-catalyzed coupling reaction.

Pd/Xu-Phos-catalyzed asymmetric elimination of fully substituted enol triflates into axially chiral trisubstituted allenes

The β-H elimination, as one of the most important elementary reactions in transition metal chemistry, is a key step in quenching the carbon-palladium bond for the Heck reaction. However, the β-H elimination of the alkenyl palladium species leading to allene is an energetically unfavored process, and therefore, it has been a long-standing challenge in control of this process via enantioselective manner. We developed a concise and efficient methodology to construct trisubstituted chiral allenes from stereodefined fully substituted enol triflates by the enantioselective β-H elimination of the alkenyl palladium species under mild conditions. The identified Xu-Phos play a crucial role in the chemoselectivity and enantioselectivity. Multiple linear regression analysis shows the important steric effect on enantioselectivity. DFT computation results allow us to propose an intramolecular base (^-OAc)-assisted deprotonation mechanism for this progress. Distortion-interaction and energy decomposition analysis indicate that the difference in electrostatic energy (E_elec) of the two intramolecular base-assisted deprotonation transition states dominates the stereoselectivity.

Computer-Driven Development of Ylide Functionalized Phosphines for Palladium-Catalyzed Hiyama Couplings

Palladium-catalyzed couplings of silicon enolates with aryl electrophiles are of great synthetic utility, but often limited to expensive bromide substrates. A comparative experimental study confirmed that none of the established ligand systems allows to couple inexpensive aryl chlorides with α-trimethylsilyl alkylnitriles. In contrast, ylide functionalized phosphines (YPhos) led to encouraging results. A statistical model was developed that correlates the reaction yields with ligand features. It was employed to predict catalyst structures with superior performance. With this cheminformatics approach, YPhos ligands were tailored specifically to the demands of Hiyama couplings. The newly synthesized ligands displayed record-setting activities, enabling the elusive coupling of aryl chlorides with α-trimethylsilyl alkyl nitriles. The preparative utility of the catalyst system was demonstrated by the synthesis of pharmaceutically meaningful α-aryl alkylnitriles, α-arylcarbonyls and biaryls.

Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis

Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data-driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles.

Boron-Catalyzed α-Functionalizations of Carboxylic Acids

Catalytic, chemoselective, and asymmetric α-functionalizations of carboxylic acids promise up-grading simple feedstock materials to value-added functional molecules, as well as late-stage structural diversifications of multifunctional molecules, such as drugs and their leads. In this personal account, we describe boron-catalyzed α-functionalizations of carboxylic acids developed in our group (five reaction types). The reversible boron carboxylate formation is key to the acidification of the α-protons and enolization using mild organic bases, allowing for chemoselective and asymmetric bond formations of carboxylic acids. The ligand effects on reactivity and stereoselectivity, substrate scopes, and mechanistic insights are summarized.

Reactivity and Enantioselectivity in NHC Organocatalysis Provide Evidence for the Complex Role of Modifications at the Secondary Sphere

Secondary-sphere interactions are often harnessed to control reactivity and selectivity in organometallic and enzymatic catalysis. Yet, such strategies have only recently been explicitly applied in the context of organocatalytic systems. Although increased stability, reproducibility, and selectivity were obtained in previous work using this approach, the precise mechanistic pathway promoted by secondary-sphere modification in organocatalysis remained unclear. Herein, we report a comprehensive mechanistic study on the origin of the unique reactivity patterns and stereocontrol observed with boronic acids (BAs) as secondary-sphere modifiers of N-heterocyclic carbene (NHC) organocatalysts. Kinetic experiments revealed partial order in catalyst upon the addition of BA and unusual preactivation behavior, indicating the presence of stable off-cycle catalyst aggregation and BA-base adducts. These hypotheses were supported both by computations and by a series of NMR and nonlinear effect experiments. Furthermore, computations indicated a rate-limiting, water-assisted hydrogen atom transfer mechanism. This finding led to a considerable enhancement in the experimental reaction rate while maintaining excellent enantioselectivity by adding catalytic amounts of water. Finally, computations and racemization experiments uncovered an uncommon Curtin-Hammett-controlled enantioselectivity in the presence of secondary-sphere modifiers.

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.

Exploring Structure-Function Relationships of Aryl Pyrrolidine-Based Hydrogen-Bond Donors in Asymmetric Catalysis Using Data-Driven Techniques

Hydrogen bond-based organocatalysts rely on networks of attractive noncovalent interactions (NCIs) to impart enantioselectivity. As a specific example, aryl pyrrolidine substituted urea, thiourea, and squaramide organocatalysts function cooperatively through hydrogen bonding and difficult-to-predict NCIs as a function of the reaction partners. To uncover the synergistic effect of the structural components of this catalyst class, we applied data science tools to study various model reactions using a derivatized, aryl pyrrolidine-based, hydrogen-bond donor (HBD) catalyst library. Through a combination of experimentally collected data and data mined from previous reports, statistical models were constructed, illuminating the general features necessary for high enantioselectivity. A distinct dependence on the identity of the electrophilic reaction partner and HBD catalyst is observed, suggesting that a general interaction is conserved throughout the reactions analyzed. The resulting models also demonstrate predictive capability by the successful improvement of a previously reported reaction using out-of-sample reaction components. Overall, this study highlights the power of data science in exploring mechanistic hypotheses in asymmetric HBD catalysis and provides a prediction platform applicable in future reaction optimization.

A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (SNAr) using simple descriptors

We report a multivariate linear regression model able to make accurate predictions for the relative rate and regioselectivity of nucleophilic aromatic substitution (S_NAr) reactions based on the electrophile structure. This model uses a diverse training/test set from experimentally-determined relative S_NAr rates between benzyl alcohol and 74 unique electrophiles, including heterocycles with multiple substitution patterns. There is a robust linear relationship between the experimental S_NAr free energies of activation and three molecular descriptors that can be obtained computationally: the electron affinity (EA) of the electrophile; the average molecular electrostatic potential (ESP) at the carbon undergoing substitution; and the sum of average ESP values for the ortho and para atoms relative to the reactive center. Despite using only simple descriptors calculated from ground state wavefunctions, this model demonstrates excellent correlation with previously measured S_NAr reaction rates, and is able to accurately predict site selectivity for multihalogenated substrates: 91% prediction accuracy across 82 individual examples. The excellent agreement between predicted and experimental outcomes makes this easy-to-implement reactivity model a potentially powerful tool for synthetic planning.

Investigating Oxidative Addition Mechanisms of Allylic Electrophiles with Low-Valent Ni/Co Catalysts Using Electroanalytical and Data Science Techniques

The catalysis by a π-allyl-Co/Ni complex has drawn significant attention recently due to its distinct reactivity in reductive Co/Ni-catalyzed allylation reactions. Despite significant success in reaction development, the critical oxidative addition mechanism to form the π-allyl-Co/Ni complex remains unclear. Herein, we present a study to investigate this process with four catalysis-relevant complexes: Co(^MeBPy)Br₂, Co(^MePhen)Br₂, Ni(^MeBPy)Br₂, and Ni(^MePhen)Br₂. Enabled by an electroanalytical platform, Co(I)/Ni(I) species were found responsible for the oxidative addition of allyl acetate. Kinetic features of different substrates were characterized through linear free-energy relationship (Hammett-type) studies, statistical modeling, and a DFT computational study. In this process, a coordination-ionization-type transition state was proposed, sharing a similar feature with Pd(0)-mediated oxidative addition in Tsuji-Trost reactions. Computational and ligand structural analysis studies support this mechanism, which should provide key information for next-generation catalyst development.

A Combined DFT, Energy Decomposition, and Data Analysis Approach to Investigate the Relationship Between Noncovalent Interactions and Selectivity in a Flexible DABCOnium/Chiral Anion Catalyst System

Developing strategies to study reactivity and selectivity in flexible catalyst systems has become an important topic of research. Herein, we report a combined experimental and computational study aimed at understanding the mechanistic role of an achiral DABCOnium cofactor in a regio- and enantiodivergent bromocyclization reaction. It was found that electron-deficient aryl substituents enable rigidified transition states via an anion-π interaction with the catalyst, which drives the selectivity of the reaction. In contrast, electron-rich aryl groups on the DABCOnium result in significantly more flexible transition states, where interactions between the catalyst and substrate are more important. An analysis of not only the lowest-energy transition state structures but also an ensemble of low-energy transition state conformers via energy decomposition analysis and machine learning was crucial to revealing the dominant noncovalent interactions responsible for observed changes in selectivity in this flexible system.

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.

Extended Connectivity Fingerprints as a Chemical Reaction Representation for Enantioselective Organophosphorus-Catalyzed Asymmetric Reaction Prediction

Predicting the outcomes of organic reactions using data-driven approaches aids in the acceleration of research. In laboratory-scale experiments, only a small number of reaction data can be accessed for machine learning model construction, where reaction representations play a pivotal role in the success of model construction. Nevertheless, representation comparison for a small data set is not adequate. Herein, focusing on the enantioselectivity of phosphoric-acid-catalyzed reactions, various two-dimensional and three-dimensional reaction representations (descriptors) were compared. Overall, the concatenated form of the extended connectivity fingerprints showed the best predictive capability for the two types of data sets: high-throughput experimental data and manually collected literature data sets. Furthermore, highlighting the substructure contribution to the prediction outcome was shown to be informative for guiding catalyst development.

Molecular field analysis for data-driven molecular design in asymmetric catalysis

This review highlights the recent advances (2019-present) in the use of MFA (molecular field analysis) for data-driven catalyst design, enabling to improve selectivities/reaction outcomes in asymmetric catalysis. Successful examples of MFA-based molecular design and how to design molecules by MFA are described, including how to generate and evaluate MFA-based regression models, and future challenges in MFA-based molecular design in molecular catalysis.

Deracemization of Binaphthyl by Suzuki Diarylation: The Role of Electronic and Steric Effects

We report a Suzuki 2,2'-diarylation of the racemic 2,2'-diiodo-1,1'-binaphthyl which proceeds with deracemization via a pallada(IV)cyclic intermediate, induced by a simple chiral ligand─BINAP [2,2'-bis(diphenylphosphino)-1,1',-binaphthyl]. A systematic study of the reaction scope, using 45 arylboronic acids, reveals that the diarylated product is formed when meta- and/or para-substituted phenylboronic acids are functionalized with a substituent with the Hammett constant from -0.5 to +0.4. Multiparametric analysis accounting for the effect of geometry on the reactivity using Boltzmann-weighted Sterimol parameters and electronic effects described by Hammett descriptors shows that the enantioselectivity depends on steric effects only, with enhanced enantioselectivity observed for substituents with a larger length, wL, and reduced for substituents with a larger maximum width, wB₅. We show that careful tuning of these parameters, with the aid of the presented mathematical model, can lead to excellent enantioselectivity. Additional factors that are investigated and found to affect the stereoselective course of the reaction include the reaction temperature, palladium source, palladium to ligand ratio, and the type of boronic acid derivative. During the chromatographic separation of diarylated products on an achiral silica gel, we observed a rare phenomenon: the diarylated products undergo self-disproportionation of enantiomers, with the major enantiomer being eluted first.

Synthetic applications of hydride abstraction reactions by organic oxidants

Carbon-hydrogen bond functionalizations provide an attractive method for streamlining organic synthesis, and many strategies have been developed for conducting these transformations. Hydride-abstracting reactions have emerged as extremely effective methods for oxidative bond-forming processes due to their mild reaction conditions and high chemoselectivity. This review will predominantly focus on the mechanism, reaction development, natural product synthesis applications, approaches to catalysis, and use in enantioselective processes for hydride abstractions by quinone, oxoammonium ion, and carbocation oxidants. These are the most commonly employed hydride-abstracting agents, but recent efforts illustrate the potential for weaker ketone and triaryl borane oxidants, which will be covered at the end of the review.