Predictive and mechanistic multivariate linear regression models for reaction development

Development of Modular Nitrenium Bipolar Electrolytes for Possible Applications in Symmetric Redox Flow Batteries

Amid the escalating integration of renewable energy sources, the demand for grid energy storage solutions, including non-aqueous organic redox flow batteries (oRFBs), has become ever more pronounced. oRFBs face a primary challenge of irreversible capacity loss attributed to the crossover of redox-active materials between half-cells. A possible solution for the crossover challenge involves utilization of bipolar electrolytes that act as both the catholyte and anolyte. Identifying such molecules poses several challenges as it requires a delicate balance between the stability of both oxidation states and energy density, which is influenced by the separation between the two redox events. We report the development of a diaminotriazolium redox-active core capable of producing two electronically distinct persistent radical species with typically extreme reduction potentials (E1/2red < -2 V, E1/2ox > +1 V, vs Fc0/+) and up to 3.55 V separation between the two redox events. Structure-property optimization studies allowed us to identify factors responsible for fine-tuning of potentials for both redox events, as well as separation between them. Mechanistic studies revealed two primary decomposition pathways for the neutral radical charged species and one for the radical biscation. Additionally, statistical modeling provided evidence for the molecular descriptors to allow identification of the structural features responsible for stability of radical species and to propose more stable analogues.

A comparison of structure, bonding and non-covalent interactions of aryl halide and diarylhalonium halogen-bond donors

Halogen bonding permeates many areas of chemistry. A wide range of halogen-bond donors including neutral, cationic, monovalent, and hypervalent have been developed and studied. In this work we used density functional theory (DFT), natural bond orbital (NBO) theory, and quantum theory of atoms in molecules (QTAIM) to analyze aryl halogen-bond donors that are neutral, cationic, monovalent and hypervalent and in each series we include the halogens Cl, Br, I, and At. Within this diverse set of halogen-bond donors, we have found trends that relate halogen bond length with the van der Waals radii of the halogen and the non-covalent or partial covalency of the halogen bond. We have also developed a model to calculate ΔG of halogen-bond formation by the linear combination of the % p-orbital character on the halogen and energy of the σ-hole on the halogen-bond donor.

Leveraging Triphenylphosphine-Containing Polymers to Explore Design Principles for Protein-Mimetic Catalysts

Complex interactions between noncoordinating residues are significant yet commonly overlooked components of macromolecular catalyst function. While these interactions have been demonstrated to impact binding affinities and catalytic rates in metalloenzymes, the roles of similar structural elements in synthetic polymeric catalysts remain underexplored. Using a model Suzuki-Miyuara cross-coupling reaction, we performed a series of systematic studies to probe the interconnected effects of metal-ligand cross-links, electrostatic interactions, and local rigidity in polymer catalysts. To achieve this, a novel bifunctional triphenylphosphine acrylamide (BisTPPAm) monomer was synthesized and evaluated alongside an analogous monofunctional triphenylphosphine acrylamide (TPPAm). In model copolymer catalysts, increased initial reaction rates were observed for copolymers untethered by Pd complexation (BisTPPAm-containing) compared to Pd-cross-linked catalysts (TPPAm-containing). Further, incorporating local rigidity through secondary structure-like and electrostatic interactions revealed nonmonotonic relationships between composition and the reaction rate, demonstrating the potential for tunable behavior through secondary-sphere interactions. Finally, through rigorous cheminformatics featurization strategies and statistical modeling, we quantitated relationships between chemical descriptors of the substrate and reaction conditions on catalytic performance. Collectively, these results provide insights into relationships among the composition, structure, and function of protein-mimetic catalytic copolymers.

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

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

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.

Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters

The ability to relate substituent electronic effects to chemical reactivity is a cornerstone of physical organic chemistry and Linear Free Energy Relationships. The computation of electronic parameters is increasingly attractive since they can be obtained rapidly for structures and substituents without available experimental data and can be applied beyond aromatic substituents, for example, in studies of transition metal complexes and aliphatic and radical systems. Nevertheless, the description of "top-down" macroscopic observables, such as Hammett parameters using a "bottom-up" computational approach, poses several challenges for the practitioner. We have examined and benchmarked the performance of various computational charge schemes encompassing quantum mechanical methods that partition charge density, methods that fit charge to physical observables, and methods enhanced by semiempirical adjustments alongside NMR values. We study the locations of the atoms used to obtain these descriptors and their correlation with empirical Hammett parameters and rate differences resulting from electronic effects. These seemingly small choices have a much more significant impact than previously imagined, which outweighs the level of theory or basis set used. We observe a wide range of performance across the different computational protocols and observe stark and surprising differences in the ability of computational parameters to capture para- vs meta-electronic effects. In general, σm predictions fare much worse than σp. As a result, the choice of where to compute these descriptors-for the ring carbons or the attached H or other substituent atoms-affects their ability to capture experimental electronic differences. Density-based schemes, such as Hirshfeld charges, are more stable toward unphysical charge perturbations that result from nearby functional groups and outperform all other computational descriptors, including several commonly used basis set based schemes such as Natural Population Analysis. Using attached atoms also improves the statistical correlations. We obtained general linear relationships for the global prediction of experimental Hammett parameters from computed descriptors for use in statistical modeling studies.

Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (Morgan fingerprints, the DRFP, the CGR representation-based Chemprop, SLATMd, B2Rl2, EquiReact and language model BERT + RXNFP) for the prediction of computed activation barriers on three diverse datasets.

Polarizability matters in enantio-selection

The prevalence of chirality, or, handedness in biological world is a fundamental phenomenon and a characteristic hallmark of life. Thus, understanding the origin of enantio-selection, i.e., the sense and magnitude of asymmetric induction, has been a long-pursued goal in asymmetric catalysis. Herein, we demonstrated a polarizability-derived electronic effect that was shown to be capable of rationalizing a broad range of stereochemical observations made in the field of asymmetric catalysis. This effect provided a consistent enantio-control model for the prediction of major enantiomers formed in a ruthenium-catalyzed asymmetric transfer hydrogenations of ketones. Direct and quantitative linear free energy relationships between substrates' local polarizabilities and observed enantio-selectivity were also revealed in three widely known asymmetric catalytic systems covering both reductions and oxidations. This broadly applicable polarizability-based electronic effect, in conjunction with conventional wisdom mainly leveraging on steric effect considerations, should aid rational design of enantio-selective processes for better production of chiral substances.

Probing the chemical 'reactome' with high-throughput experimentation data

High-throughput experimentation (HTE) has the potential to improve our understanding of organic chemistry by systematically interrogating reactivity across diverse chemical spaces. Notable bottlenecks include few publicly available large-scale datasets and the need for facile interpretation of these data's hidden chemical insights. Here we report the development of a high-throughput experimentation analyser, a robust and statistically rigorous framework, which is applicable to any HTE dataset regardless of size, scope or target reaction outcome, which yields interpretable correlations between starting material(s), reagents and outcomes. We improve the HTE data landscape with the disclosure of 39,000+ previously proprietary HTE reactions that cover a breadth of chemistry, including cross-coupling reactions and chiral salt resolutions. The high-throughput experimentation analyser was validated on cross-coupling and hydrogenation datasets, showcasing the elucidation of statistically significant hidden relationships between reaction components and outcomes, as well as highlighting areas of dataset bias and the specific reaction spaces that necessitate further investigation.

Enantioselective Sulfonimidamide Acylation via a Cinchona Alkaloid-Catalyzed Desymmetrization: Scope, Data Science, and Mechanistic Investigation

Methods to access chiral sulfur(VI) pharmacophores are of interest in medicinal and synthetic chemistry. We report the desymmetrization of unprotected sulfonimidamides via asymmetric acylation with a cinchona-phosphinate catalyst. The desired products are formed in excellent yield and enantioselectivity with no observed bis-acylation. A data-science-driven approach to substrate scope evaluation was coupled to high throughput experimentation (HTE) to facilitate statistical modeling in order to inform mechanistic studies. Reaction kinetics, catalyst structural studies, and density functional theory (DFT) transition state analysis elucidated the turnover-limiting step to be the collapse of the tetrahedral intermediate and provided key insights into the catalyst-substrate structure-activity relationships responsible for the origin of the enantioselectivity. This study offers a reliable method for accessing enantioenriched sulfonimidamides to propel their application as pharmacophores and serves as an example of the mechanistic insight that can be gleaned from integrating data science and traditional physical organic techniques.

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.

Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure-Activity Relationships

In the pursuit of optimal quantitative structure-activity relationship (QSAR) models, two key factors are paramount: the robustness of predictive ability and the interpretability of the model. Symbolic regression (SR) searches for the mathematical expressions that explain a training data set. Thus, the models provided by SR are globally interpretable. We previously proposed an SR method that can generate interpretable expressions by humans. This study introduces an enhanced symbolic regression method, termed filter-induced genetic programming 2 (FIGP2), as an extension of our previously proposed SR method. FIGP2 is designed to improve the generalizability of SR models and to be applicable to data sets in which cost-intensive descriptors are employed. The FIGP2 method incorporates two major improvements: a modified domain filter to eradicate diverging expressions based on optimal calculation and the introduction of a stability metric to penalize expressions that would lead to overfitting. Our retrospective comparative analysis using 12 structure-activity relationship data sets revealed that FIGP2 surpassed the previously proposed SR method and conventional modeling methods, such as support vector regression and multivariate linear regression in terms of predictive performance. Generated mathematical expressions by FIGP2 were relatively simple and not divergent in the domain of function. Taken together, FIGP2 can be used for making interpretable regression models with predictive ability.

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.

On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set

Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments.

%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.

Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis

The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine's process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries.

Quantitative Structure-Reactivity Relationships for Synthesis Planning: The Benzhydrylium Case

Selective and feasible reactions are among the top targets in synthesis planning. Mayr's approach to quantifying chemical reactivity has greatly facilitated the planning process, but reactivity parameters for new compounds require time-consuming experiments. In the past decade, data-driven modeling has been gaining momentum in the field, as it shows promise in terms of efficient reactivity prediction. However, state-of-the-art models use quantum chemical data as input, which prevent access to real-time planning in organic synthesis. Here, we present a novel data-driven workflow for predicting reactivity parameters of molecules that takes only structural information as input, enabling de facto real-time reactivity predictions. We use the well-understood chemical space of benzhydrylium ions as an example to demonstrate the functionality of our approach and the performance of the resulting quantitative structure-reactivity relationships (QSRRs). Our results suggest that it is straightforward to build low-cost QSRR models that are accurate, interpretable, and transferable to unexplored systems within a given scope of application. Moreover, our QSRR approach suggests that Hammett σ parameters are only approximately additive.

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.

Data science enabled discovery of a highly soluble 2,2'-bipyrimidine anolyte for application in a flow battery

Development of non-aqueous redox flow batteries as a viable energy storage solution relies upon the identification of soluble charge carriers capable of storing large amounts of energy over extended time periods. A combination of metrics including number of electrons stored per molecule, redox potential, stability, and solubility of the charge carrier impact performance. In this context, we recently reported a 2,2'-bipyrimidine charge carrier that stores two electrons per molecule with reduction near -2.0 V vs. Fc/Fc+ and high stability. However, these first-generation derivatives showed a modest solubility of 0.17 M (0.34 M e-). Seeking to improve solubility without sacrificing stability, we harnessed the synthetic modularity of this scaffold to design a library of sixteen candidates. Using computed molecular descriptors and a single node decision tree, we found that minimization of the solvent accessible surface area (SASA) can be used to predict derivatives with enhanced solubility. This parameter was used in combination with a heatmap describing stability to de-risk a virtual screen that ultimately identified a 2,2'-bipyrimidine with significantly increased solubility and good stability metrics in the reduced states. This molecule was paired with a cyclopropenium catholyte in a prototype all-organic redox flow battery, achieving a cell potential up to 3 V.

Fragment contribution models for predicting skin permeability using HuskinDB

Mathematical models to predict skin permeation tend to be based on animal derived experimental data as well as knowing physicochemical properties of the compound under investigation, such as molecular volume, polarity and lipophilicity. This paper presents a strikingly contrasting model to predict permeability, formed entirely from simple chemical fragment (functional group) data and a recently released, freely accessible human (i.e. non-animal) skin permeation database, known as the 'Human Skin Database - HuskinDB'. Data from within the database allowed development of several fragment-based models, each including a calculable effect for all of the most commonly encountered functional groups present in compounds within the database. The developed models can be applied to predict human skin permeability (logKp) for any compound containing one or more of the functional groups analysed from the dataset with no need to know any other physicochemical properties, solely the type and number of each functional group within the chemical structure itself. This approach simplifies mathematical prediction of permeability for compounds with similar properties to those used in this study.

Zeolite encapsulated organometallic complexes as model catalysts

Heterogeneities in the structure of active centers in metal-containing porous materials are unavoidable and complicate the description of chemical events occurring along reaction coordinates at the atomic level. Metal containing zeolites include sites of varied local coordination and secondary confining environments, requiring careful titration protocols to quantify the predominant active sites. Hybrid organometallic-zeolite catalysts are useful well-defined platform materials for spectroscopic, kinetic, and computational studies of heterogeneous catalysis that avoid the complications of conventional metal-containing porous materials. Such materials have been synthesized and studied previously, but catalytic applications were mostly limited to liquid-phase oxidation and electrochemical reactions. The hydrothermal stability, time-on-stream stability, and utility of these materials in gas-phase oxidation reactions are under-studied. The potential applications for single-site heterogeneous catalysts in fundamental research are abundant and motivate future synthetic, spectroscopic, kinetic, and computational studies.

Identifying structure-function relationships to modulate crossover in nonaqueous redox flow batteries

Nonaqueous redox flow batteries (NARFBs) offer a promising solution for large-scale storage of renewable energy. However, crossover of redox active molecules between the two sides of the cell is a major factor limiting their development, as most selective separators are designed for deployment in water, rather than organic solvents. This report describes a systematic investigation of the crossover rates of redox active organic molecules through an anion exchange separator under RFB-relevant non-aqueous conditions (in acetonitrile/KPF6) using a combination of experimental and computational methods. A structurally diverse set of neutral and cationic molecules was selected, and their rates of crossover were determined experimentally with the organic solvent-compatible anion exchange separator Fumasep FAP-375-PP. The resulting data were then fit to various descriptors of molecular size, charge, and hydrophobicity (overall charge, solution diffusion coefficient, globularity, dynamic volume, dynamic surface area, clogP). This analysis resulted in multiple statistical models of crossover rates for this separator. These models were then used to predict tether groups that dramatically slow the crossover of small organic molecules in this system.

Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence

The use of micro/meso-fluidic reactors has resulted in both new scenarios for chemistry and new requirements for chemists. Through flow chemistry, large-scale reactions can be performed in drastically reduced reactor sizes and reaction times. This obvious advantage comes with the concomitant challenge of re-designing long-established batch processes to fit these new conditions. The reliance on experimental trial-and-error to perform this translation frequently makes flow chemistry unaffordable, thwarting initial aspirations to revolutionize chemistry. By combining computational chemistry and machine learning, we have developed a model that provides predictive power tailored specifically to flow reactions. We show its applications to translate batch to flow, to provide mechanistic insight, to contribute reagent descriptors, and to synthesize a library of novel compounds in excellent yields after executing a single set of conditions.

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.

The consumer price index prediction using machine learning approaches: Evidence from the United States

The consumer price index (CPI) is one of the most important macroeconomic indicators for determining inflation, and accurate predictions of CPI changes are important for a country's economic development. This study uses multivariate linear regression (MLR), support vector regression (SVR), autoregressive distributed lag (ARDL), and multivariate adaptive regression splines (MARS) to predict the CPI of the United States. Data from January 2017 to February 2022 were randomly selected and divided into two stages: 80 % for training and 20% for testing. The US CPI was modeled for the observed period and relied on a mix of elements, including crude oil price, world gold price, and federal fund effective rate. Evaluation metrics-mean absolute percentage value, mean absolute error, root mean square error, R-squared, and correlation of determination-were employed to estimate forecasted values. The MLR, SVR, ARDL, and MARS models attained high accuracy parameters, while the MARS algorithm generated higher accuracy in US CPI forecasts than the others in the testing phase. These outputs could support the US government in overseeing economic policies, sectors, and social security, thereby boosting national economic development.

SMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions

Recent advances in machine learning have led to the rapid adoption of various computational methods for de novo molecular design in polymer research, including high-throughput virtual screening and inverse molecular design. In such workflows, molecular generators play an essential role in creation or sequential modification of candidate polymer structures. Machine learning-assisted molecular design has made great technical progress over the past few years. However, the difficulty of identifying synthetic routes to such designed polymers remains unresolved. To address this technical limitation, we present Small Molecules into Polymers (SMiPoly), a Python library for virtual polymer generation that implements 22 chemical rules for commonly applied polymerization reactions. For given small organic molecules to form a candidate monomer set, the SMiPoly generator conducts possible polymerization reactions to generate an exhaustive list of potentially synthesizable polymers. In this study, using 1083 readily available monomers, we generated 169,347 unique polymers forming seven different molecular types: polyolefin, polyester, polyether, polyamide, polyimide, polyurethane, and polyoxazolidone. By comparing the distribution of the virtually created polymers with approximately 16,000 real polymers synthesized so far, it was found that the coverage and novelty of the SMiPoly-generated polymers can reach 48 and 53%, respectively. Incorporating the SMiPoly library into a molecular design workflow will accelerate the process of de novo polymer synthesis by shortening the step to select synthesizable candidate polymers.

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.

Functionalized Cyclopentenones with Low Electrophilic Character as Anticancer Agents

In this study were synthesized non-Michael acceptor cyclopentenones (CP) from biomass derivative furfural as anticancer agents. Cyclic enones, both from natural sources and synthetic analogues, have been described as cytotoxic agents. Most of these agents were unsuccessful in becoming valuable therapeutic agents due to toxicity problems derived from unselective critical biomacromolecule alkylation. This may be caused by Michael addition to the enone system. Ab initio studies revealed that 2,4-substituted CPs are less prone to Michael additions, and as such were tested three families of those derivatives. We prepare the new CPs from furfural through a tandem furan ring opening/Nazarov electrocyclization and further functionalization. Experimentally the 2,4-substituted CPs exhibited no reactivity towards sulphur nucleophiles, while maintaining cytotoxicity against HT-29, MCF-7, NCI-H460, HCT-116 and MDA-MB 231 cells lines. Moreover, the selected CP are non-toxic against healthy HEK 293T cell lines and present proper calculated drug-like properties.

Data Science Enables the Development of a New Class of Chiral Phosphoric Acid Catalysts

The widespread success of BINOL-chiral phosphoric acids (CPAs) has led to the development of several high molecular weight, sterically encumbered variants. Herein, we disclose an alternative, minimalistic chiral phosphoric acid backbone incorporating only a single instance of point chirality. Data science techniques were used to select a diverse training set of catalysts, which were benchmarked against the transfer hydrogenation of an 8-aminoquinoline. Using a univariate classification algorithm and multivariate linear regression, key catalyst features necessary for high levels of selectivity were deconvoluted, revealing a simple catalyst model capable of predicting selectivity for out-of-set catalysts. This workflow enabled extrapolation to a catalyst providing higher selectivity than both reported peptide-type and BINOL-type catalysts (up to 95:5 er). These techniques were then successfully applied towards two additional transforms. Taken together, these examples illustrate the power of combining rational design with data science (ab initio) to efficiently explore reactivity during catalyst development.

Determination of Enantiomeric Excess and Diastereomeric Excess via Optical Methods. Application to α-methyl-β-hydroxy-carboxylic acids

Characterization of chiral molecules in solution is paramount for measuring reaction success. However, techniques to distinguish between chiral molecules containing more than one stereocenter through the use of optical techniques remains a challenge. Herein, we report a techique using a series of circular dichroism spectra to train multivariate regression models that are capable of predicting the complete speciation of 3-hydroxy-2-methylbutanoic acid stereoisomers. From this, it is possible to rapidly and accurately determine the enantiomeric excess and diastereomeric excess of the solution without the need for chiral chromatography.

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors

Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time- and cost-efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine-tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis.

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.

Transferrable selectivity profiles enable prediction in synergistic catalyst space

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

1-Fluoro-1-sulfonyloxylation of Alkenes by Sterically and Electronically Tuned Hypervalent Iodine: Regression Analysis toward 1,1-Heterodifunctionalization

In the heterodifunctionalization of alkenes, 1,1-regioselectivity remains elusive in sharp contrast to 1,2-regioselectivity. Herein, the 1-fluoro-1-sulfonyloxylation of styrenes with Bu₄NBF₄ and sulfonic acids using a hypervalent iodine ArI(OAc)₂ is reported. Regression analysis of substituents on ArI(OAc)₂ suggested that their electron-withdrawing ability and steric factor influence the 1,1-heterodifunctionalization. We designed o-{2,4-(CF₃)₂C₆H₃}- and p-NO₂-substituted ArI(OAc)₂ by the regression analysis to achieve high selectivity.

Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis-Challenges and Opportunities

Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.

Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >550 bisphosphine ligands was constructed, and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs, suggesting a general strategy that can be readily implemented for reaction optimizations where performance is controlled by bisphosphine ligands.

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.

Structure-Reactivity-Property Relationships in Covalent Adaptable Networks

Polymer networks built out of dynamic covalent bonds offer the potential to translate the control and tunability of chemical reactions to macroscopic physical properties. Under conditions at which these reactions occur, the topology of covalent adaptable networks (CANs) can rearrange, meaning that they can flow, self-heal, be remolded, and respond to stimuli. Materials with these properties are necessary to fields ranging from sustainability to tissue engineering; thus the conditions and time scale of network rearrangement must be compatible with the intended use. The mechanical properties of CANs are based on the thermodynamics and kinetics of their constituent bonds. Therefore, strategies are needed that connect the molecular and macroscopic worlds. In this Perspective, we analyze structure-reactivity-property relationships for several classes of CANs, illustrating both general design principles and the predictive potential of linear free energy relationships (LFERs) applied to CANs. We discuss opportunities in the field to develop quantitative structure-reactivity-property relationships and open challenges.

Catalytic asymmetric C-H insertion reactions of vinyl carbocations

From the preparation of pharmaceuticals to enzymatic construction of natural products, carbocations are central to molecular synthesis. Although these reactive intermediates are engaged in stereoselective processes in nature, exerting enantiocontrol over carbocations with synthetic catalysts remains challenging. Many resonance-stabilized tricoordinated carbocations, such as iminium and oxocarbenium ions, have been applied in catalytic enantioselective reactions. However, their dicoordinated counterparts (aryl and vinyl carbocations) have not, despite their emerging utility in chemical synthesis. We report the discovery of a highly enantioselective vinyl carbocation carbon-hydrogen (C-H) insertion reaction enabled by imidodiphosphorimidate organocatalysts. Active site confinement featured in this catalyst class not only enables effective enantiocontrol but also expands the scope of vinyl cation C-H insertion chemistry, which broadens the utility of this transition metal-free C(sp³)-H functionalization platform.

Data Science-Driven Analysis of Substrate-Permissive Diketopiperazine Reverse Prenyltransferase NotF: Applications in Protein Engineering and Cascade Biocatalytic Synthesis of (-)-Eurotiumin A

Prenyltransfer is an early-stage carbon-hydrogen bond (C-H) functionalization prevalent in the biosynthesis of a diverse array of biologically active bacterial, fungal, plant, and metazoan diketopiperazine (DKP) alkaloids. Toward the development of a unified strategy for biocatalytic construction of prenylated DKP indole alkaloids, we sought to identify and characterize a substrate-permissive C2 reverse prenyltransferase (PT). As the first tailoring event within the biosynthesis of cytotoxic notoamide metabolites, PT NotF catalyzes C2 reverse prenyltransfer of brevianamide F. Solving a crystal structure of NotF (in complex with native substrate and prenyl donor mimic dimethylallyl S-thiolodiphosphate (DMSPP)) revealed a large, solvent-exposed active site, intimating NotF may possess a significantly broad substrate scope. To assess the substrate selectivity of NotF, we synthesized a panel of 30 sterically and electronically differentiated tryptophanyl DKPs, the majority of which were selectively prenylated by NotF in synthetically useful conversions (2 to >99%). Quantitative representation of this substrate library and development of a descriptive statistical model provided insight into the molecular origins of NotF's substrate promiscuity. This approach enabled the identification of key substrate descriptors (electrophilicity, size, and flexibility) that govern the rate of NotF-catalyzed prenyltransfer, and the development of an "induced fit docking (IFD)-guided" engineering strategy for improved turnover of our largest substrates. We further demonstrated the utility of NotF in tandem with oxidative cyclization using flavin monooxygenase, BvnB. This one-pot, in vitro biocatalytic cascade enabled the first chemoenzymatic synthesis of the marine fungal natural product, (-)-eurotiumin A, in three steps and 60% overall yield.

Noncovalent Stabilization of Radical Intermediates in the Enantioselective Hydroamination of Alkenes with Sulfonamides

Noncovalent interactions (NCIs) are critical elements of molecular recognition in a wide variety of chemical contexts. While NCIs have been studied extensively for closed-shell molecules and ions, very little is understood about the structures and properties of NCIs involving free radical intermediates. In this report, we describe a detailed mechanistic study of the enantioselective radical hydroamination of alkenes with sulfonamides and present evidence suggesting that the basis for asymmetric induction in this process arises from attractive NCIs between a neutral sulfonamidyl radical intermediate and a chiral phosphoric acid (CPA). We describe experimental, computational, and data science-based evidence that identifies the specific radical NCIs that form the basis for the enantioselectivity. Kinetic studies support that C-N bond formation determines the enantioselectivity. Density functional theory investigations revealed the importance of both strong H-bonding between the CPA and the N-centered radical and a network of aryl-based NCIs that serve to stabilize the favored diastereomeric transition state. The contributions of these specific aryl-based NCIs to the selectivity were further confirmed through multivariate linear regression analysis by comparing the measured enantioselectivity to computed descriptors. These results highlight the power of NCIs to enable high levels of enantioselectivity in reactions involving uncharged open-shell intermediates and expand our understanding of radical-molecule interactions.

Theoretical Perspectives in Organocatalysis

It is clear that the field of organocatalysis is continuously expanding during the last decades. With increasing computational capacity and new techniques, computational methods have provided a more economic approach to explore different chemical systems. This review offers a broad yet concise overview of current state-of-the-art studies that have employed novel strategies for catalyst design. The evolution of the all different theoretical approaches most commonly used within organocatalysis is discussed, from the traditional approach, manual-driven, to the most recent one, machine-driven.

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.

Data-Driven Prediction of Circular Dichroism-Based Calibration Curves for the Rapid Screening of Chiral Primary Amine Enantiomeric Excess Values

Here, we describe the prediction of the circular dichroism (CD) response of a three-component chiroptical sensor for enantiomeric excess (ee) determination of chiral amines using a multivariate fit to electronic and steric parameters. These computationally derived parameters can be computed for nearly any amine and correlate well with the CD response of the 12 amines comprising the training set. The resulting model was used to accurately predict the CD response of a test set of chiral amines. Theoretical calibration curves were then created and used to determine the ee of solutions of unknown ee. Using this method, the error in ee determination differed by less than 10% compared to experimentally generated calibration curves.