On the Topic of Substrate Scope

Reaction development: a student's checklist

So you've discovered a reaction. But how do you turn this new discovery into a fully-fledged program that maximises the potential of your novel transformation? Herein, we provide a student's checklist to serve as a helpful guide for synthesis development, allowing you to thoroughly investigate the chemistry in question while ensuring that no key aspect of the project is overlooked. A wide variety of the most illuminating synthetic and spectroscopic techniques will be summarised, in conjunction with literature examples and our own insights, to provide sound justifications for their implementation towards the goal of developing new reactions.

Revealing the Relationship between Publication Bias and Chemical Reactivity with Contrastive Learning

A synthetic method's substrate tolerance and generality are often showcased in a "substrate scope" table. However, substrate selection exhibits a frequently discussed publication bias: unsuccessful experiments or low-yielding results are rarely reported. In this work, we explore more deeply the relationship between such a publication bias and chemical reactivity beyond the simple analysis of yield distributions using a novel neural network training strategy, substrate scope contrastive learning. By treating reported substrates as positive samples and nonreported substrates as negative samples, our contrastive learning strategy teaches a model to group molecules within a numerical embedding space, based on historical trends in published substrate scope tables. Training on 20,798 aryl halides in the CAS Content CollectionTM, spanning thousands of publications from 2010 to 2015, we demonstrate that the learned embeddings exhibit a correlation with physical organic reactivity descriptors through both intuitive visualizations and quantitative regression analyses. Additionally, these embeddings are applicable to various reaction modeling tasks like yield prediction and regioselectivity prediction, underscoring the potential to use historical reaction data as a pretraining task. This work not only presents a chemistry-specific machine learning training strategy to learn from literature data in a new way but also represents a unique approach to uncover trends in chemical reactivity reflected by trends in substrate selection in publications.

Chiral pyrrolidines via an enantioselective Hofmann-Löffler-Freytag reaction

Radical C-H aminations enable rapid access to the most common heterocycles in medicines (e.g. pyrrolidines), yet stereocontrol of these powerful transformations remains a challenge. Here, we report the discovery of the first enantio- and regio- selective C-H imination, which readily converts ketones to enantioenriched pyrrolidines. This enantioselective Hofmann-Löffler-Freytag reaction mechanism entails iminyl radical generation from an oxime by a chiral Cu catalyst that facilitates 1,5-H-atom transfer (HAT) to form a remote C-radical, regioselectively. The selective capture of this alkyl radical as an organocopper(III) complex then mediates highly stereoselective reductive elimination to unprotected pyrrolines. The broad steric and electronic scope of this remote C-H amination has been probed systematically, along with key mechanistic aspects of enantiodetermination, radical intermediacy, and atypical Cu(III) ligands that enable this uniquely selective C-N coupling. Importantly, either (1) reductions or (2) nucleophilic additions to these enantioenriched pyrrolines provide the most rapid syntheses of chiral pyrrolidines to date.

Accessing Active Fragments for Drug Discovery Utilising Nitroreductase Biocatalysis

Biocatalysis has played a limited role in the early stages of drug discovery. This is often attributed to the limited substrate scope of enzymes not affording access to vast areas of novel chemical space. Here, we have shown a promiscuous nitroreductase enzyme (NR-55) can be used to produce a panel of functionalised anilines from a diverse panel of aryl nitro starting materials. After screening on analytical scale, we show that sixteen substrates could be scaled to 1 mmol scale, with several poly-functional anilines afforded with ease under the standard conditions. The aniline products were also screened for activity against several cell lines of interest, with modest activity observed for one compound. This study demonstrates the potential for nitroreductase biocatalysis to provide access to functional fragments under benign conditions.

Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality

With over 10,000 new reaction protocols arising every year, only a handful of these procedures transition from academia to application. A major reason for this gap stems from the lack of comprehensive knowledge about a reaction's scope, i.e., to which substrates the protocol can or cannot be applied. Even though chemists invest substantial effort to assess the scope of new protocols, the resulting scope tables involve significant biases, reducing their expressiveness. Herein we report a standardized substrate selection strategy designed to mitigate these biases and evaluate the applicability, as well as the limits, of any chemical reaction. Unsupervised learning is utilized to map the chemical space of industrially relevant molecules. Subsequently, potential substrate candidates are projected onto this universal map, enabling the selection of a structurally diverse set of substrates with optimal relevance and coverage. By testing our methodology on different chemical reactions, we were able to demonstrate its effectiveness in finding general reactivity trends by using a few highly representative examples. The developed methodology empowers chemists to showcase the unbiased applicability of novel methodologies, facilitating their practical applications. We hope that this work will trigger interdisciplinary discussions about biases in synthetic chemistry, leading to improved data quality.

Development of High-Throughput Experimentation Approaches for Rapid Radiochemical Exploration

Positron emission tomography is a widely used imaging platform for studying physiological processes. Despite the proliferation of modern synthetic methodologies for radiolabeling, the optimization of these reactions still primarily relies on inefficient one-factor-at-a-time approaches. High-throughput experimentation (HTE) has proven to be a powerful approach for optimizing reactions in many areas of chemical synthesis. However, to date, HTE has rarely been applied to radiochemistry. This is largely because of the short lifetime of common radioisotopes, which presents major challenges for efficient parallel reaction setup and analysis using standard equipment and workflows. Herein, we demonstrate an effective HTE workflow and apply it to the optimization of copper-mediated radiofluorination of pharmaceutically relevant boronate ester substrates. The workflow utilizes commercial equipment and allows for rapid analysis of reactions for optimizing reactions, exploring chemical space using pharmaceutically relevant aryl boronates for radiofluorinations, and constructing large radiochemistry data sets.

Bridging the information gap in organic chemical reactions

The varying quality of scientific reports is a well-recognized problem and often results from a lack of standardization and transparency in scientific publications. This situation ultimately leads to prominent complications such as reproducibility issues and the slow uptake of newly developed synthetic methods for pharmaceutical and agrochemical applications. In recent years, various impactful approaches have been advocated to bridge information gaps and to improve the quality of experimental protocols in synthetic organic publications. Here we provide a critical overview of these strategies and present the reader with a versatile set of tools to augment their standard procedures. We formulate eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards. We are aware that this is a substantial effort, but we are convinced that the resulting improved data situation will greatly benefit the progress of chemistry.

A Modular Strategy for the Synthesis of Macrocycles and Medium-Sized Rings via Cyclization/Ring Expansion Cascade Reactions

Macrocycles and medium-sized rings are important in many scientific fields and technologies but are hard to make using current methods, especially on a large scale. Outlined herein is a strategy by which functionalized macrocycles and medium-sized rings can be prepared using cyclization/ring expansion (CRE) cascade reactions, without resorting to high dilution conditions. CRE cascade reactions are designed to operate exclusively via kinetically favorable 5-7-membered ring cyclization steps; this means that the problems typically associated with classical end-to-end macrocyclization reactions are avoided. A modular synthetic approach has been developed to facilitate the simple assembly of the requisite linear precursors, which can then be converted into an extremely broad range of functionalized macrocycles and medium-sized rings using one of nine CRE protocols.

Identifying general reaction conditions by bandit optimization

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries1-6. Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models7-10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C-H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.

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.

Branched-Selective Cross-Electrophile Coupling of 2-Alkyl Aziridines and (Hetero)aryl Iodides Using Ti/Ni Catalysis

The arylation of 2-alkyl aziridines by nucleophilic ring-opening or transition-metal-catalyzed cross-coupling enables facile access to biologically relevant β-phenethylamine derivatives. However, both approaches largely favor C-C bond formation at the less-substituted carbon of the aziridine, thus enabling access to only linear products. Consequently, despite the attractive bond disconnection that it poses, the synthesis of branched arylated products from 2-alkyl aziridines has remained inaccessible. Herein, we address this long-standing challenge and report the first branched-selective cross-coupling of 2-alkyl aziridines with aryl iodides. This unique selectivity is enabled by a Ti/Ni dual-catalytic system. We demonstrate the robustness of the method by a twofold approach: an additive screening campaign to probe functional group tolerance and a feature-driven substrate scope to study the effect of the local steric and electronic profile of each coupling partner on reactivity. Furthermore, the diversity of this feature-driven substrate scope enabled the generation of predictive reactivity models that guided mechanistic understanding. Mechanistic studies demonstrated that the branched selectivity arises from a TiIII-induced radical ring-opening of the aziridine.

Hydrogen-Bonding Ability of Noyori-Ikariya Catalysts Enables Stereoselective Access to CF3-Substituted syn-1,2-Diols via Dynamic Kinetic Resolution

Stereopure CF3-substituted syn-1,2-diols were prepared via the reductive dynamic kinetic resolution of the corresponding racemic α-hydroxyketones in HCO2H/Et3N. (Het)aryl, benzyl, vinyl, and alkyl ketones are tolerated, delivering products with ≥95% ee and ≥87:13 syn/anti. This methodology offers rapid access to stereopure bioactive molecules. Furthermore, DFT calculations for three types of Noyori-Ikariya ruthenium catalysts were performed to show their general ability of directing stereoselectivity via the hydrogen bond acceptor SO2 region and CH/π interactions.

Ring Expansion Strategies for the Synthesis of Medium Sized Ring and Macrocyclic Sulfonamides

Two new ring expansion strategies are reported for the synthesis of medium sized ring and macrocyclic sulfonamides. Both methods can be performed without using classical protecting groups, with the key ring expansion step initiated by nitro reduction and amine conjugate addition respectively. Each method can be used to make diversely functionalised cyclic sulfonamides in good to excellent yields, in a range of ring sizes. The ring size dependency of the synthetic reactions is in good agreement with the outcomes modelled by Density Functional Theory calculations.

Synthesis of Isoquinoline-Derived Diene Esters and Quinolin-2(1H)-ylidene-Substituted 1,5-Diones from Enynones and (Iso) Quinoline N-Oxides

An efficient synthetic method was developed to access isoquinoline-derived diene esters from enynones and isoquinoline-N-oxides in an atom-economic manner. The isoquinoline-substituted diene esters were obtained in moderate to excellent yields via [3 + 2]-cycloaddition and isoxazole ring opening followed by a [1,5]-sigmatropic rearrangement reaction, which resulted in one C-C and two C-O bond formations. Further, quinolin-2(1H)-ylidene-substituted 1,5-diones were achieved by reaction of enynones with quinoline-N-oxides in very good to high yields.