Interrogation of Enantioselectivity in the Photomediated Ring Contractions of Saturated Heterocycles.

We recently reported a chiral phosphoric acid (CPA) catalyzed enantioselective photomediated ring contraction of piperidines and other saturated heterocycles. By extruding a single heteroatom from a ring, this transformation builds desirable C(sp)-C(sp) bonds in the ring contracted products; however, the origins of enantioselectivity remain poorly understood. In this work, enantioselectivity of the ring contraction has been explored across an expanded structurally diverse substrate scope, revealing a wide range of enantioselectivities (0-99%) using two distinct CPA catalysts.

Applying statistical modeling strategies to sparse datasets in synthetic chemistry.

The application of statistical modeling in organic chemistry is emerging as a standard practice for probing structure-activity relationships and as a predictive tool for many optimization objectives. This review is aimed as a tutorial for those entering the area of statistical modeling in chemistry. We provide case studies to highlight the considerations and approaches that can be used to successfully analyze datasets in low data regimes, a common situation encountered given the experimental demands of organic chemistry.

Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines.

Data-driven reaction discovery and development is a growing field that relies on the use of molecular descriptors to capture key information about substrates, ligands, and targets. Broad adaptation of this strategy is hindered by the associated computational cost of descriptor calculation, especially when considering conformational flexibility. Descriptor libraries can be precomputed agnostic of application to reduce the computational burden of data-driven reaction development.

Computational Library Enables Pattern Recognition of Noncovalent Interactions and Application as a Modern Linear Free Energy Relationship.

A quantitative and predictive understanding of how attractive noncovalent interactions (NCIs) influence functional outcomes is a long-standing goal in mechanistic chemistry. In that context, better comprehension of how substituent effects influence NCI strengths, and the origin of those effects, is still needed. We sought to build a resource capable of elucidating fundamental origins of substituent effects in NCIs and diagnosing NCIs in chemical systems.

Development of a Deactivation-Resistant Dialkylbiarylphosphine Ligand for Pd-Catalyzed Arylation of Secondary Amines.

Despite the prevalence of N-heteroarenes in small-molecule pharmaceuticals, Pd-catalyzed C-N cross-coupling reactions of aryl halides and amines containing these rings remain challenging due to their ability to displace the supporting ligand via coordination to the metal center. To address this limitation, we report the development of a highly robust Pd catalyst supported by a new dialkylbiarylphosphine ligand, FPhos. The FPhos-supported catalyst effectively resists N-heteroarene-mediated catalyst deactivation to readily promote C-N coupling between a wide variety of Lewis-basic aryl halides and secondary amines, including densely functionalized pharmaceuticals.

Small-molecule properties define partitioning into biomolecular condensates.

Biomolecular condensates regulate cellular function by compartmentalizing molecules without a surrounding membrane. Condensate function arises from the specific exclusion or enrichment of molecules. Thus, understanding condensate composition is critical to characterizing condensate function.

Modular synthesis of aryl amines from 3-alkynyl-2-pyrones.

The synthesis of aryl amines from 3-alkynyl-2-pyrones and various amines is described. Mechanistically, the aryl amines are proposed to arise from the 3-alkynyl-2-pyrone substrates through their selective opening in a 1,6-fashion by secondary amines followed by decarboxylation and an unexpected rearrangement. The proposed mechanism is supported by quantum chemical transition-state calculations, which are consistent with the regiochemical outcome.

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.

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 (, structure-polymerization kinetics relationships) and serve as practical tools for the polymer chemist (, post-polymerization functionalization). In this work, a library of TCN and TCND imides, monoesters, and diesters, along with their -norbornene counterparts, were synthesized to compare their behaviors in G3-initiated ROMP.

Using Data Science Tools to Reveal and Understand Subtle Relationships of Inhibitor Structure in Frontal Ring-Opening Metathesis Polymerization.

The rate of frontal ring-opening metathesis polymerization (FROMP) using the Grubbs generation II catalyst is impacted by both the concentration and choice of monomers and inhibitors, usually organophosphorus derivatives. Herein we report a data-science-driven workflow to evaluate how these factors impact both the rate of FROMP and how long the formulation of the mixture is stable (pot life). Using this workflow, we built a classification model using a single-node decision tree to determine how a simple phosphine structural descriptor () can bin long versus short pot life.

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry.

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.

Electrocatalytic Asymmetric Nozaki-Hiyama-Kishi Decarboxylative Coupling: Scope, Applications, and Mechanism.

The first general enantioselective alkyl-Nozaki-Hiyama-Kishi (NHK) coupling reactions are disclosed herein by employing a Cr-electrocatalytic decarboxylative approach. Using easily accessible aliphatic carboxylic acids (via redox-active esters) as alkyl nucleophile synthons, in combination with aldehydes and enabling additives, chiral secondary alcohols are produced in a good yield with high enantioselectivity under mild reductive electrolysis. This reaction, which cannot be mimicked using stoichiometric metal or organic reductants, tolerates a broad range of functional groups and is successfully applied to dramatically simplify the synthesis of multiple medicinally relevant structures and natural products.

N to N Isotopic Exchange of Nitrogen Heteroaromatics through Skeletal Editing.

The selective modification of nitrogen heteroaromatics enables the development of new chemical tools and accelerates drug discovery. While methods that focus on expanding or contracting the skeletal structures of heteroaromatics are emerging, methods for the direct exchange of single core atoms remain limited. Here, we present a method for N → N isotopic exchange for several aromatic nitrogen heterocycles.

Predicting success in Cu-catalyzed C-N coupling reactions using data science.

Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions.

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/KPF) using a combination of experimental and computational methods.

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.

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.

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.

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.

Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides.

New methods for the general asymmetric synthesis of sulfonimidamides are of great interest due to their applications in medicinal chemistry, agrochemical discovery, and academic research. We report a palladium-catalyzed cross-coupling method for the enantioselective aryl-carbonylation of sulfonimidamides. Using data science techniques, a virtual library of calculated bisphosphine ligand descriptors was used to guide reaction optimization by effectively sampling the catalyst chemical space.

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.

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.