Switchable and Chemoselective Arene Hydrogenation for Efficient Late Stage Applications.

The incorporation of three-dimensional structures into drug molecules has demonstrated significant improvements in clinical success. Late-stage saturation of drug molecules provides a direct pathway for this transformation. However, achieving selective and controllable reduction of aromatic rings remains challenging, particularly when multiple aromatic rings coexist. Herein, we present the switchable and chemoselective hydrogenation of benzene and pyridine rings. The utility of the protocol has been comprehensively investigated in diversified substrates with the assistance of a fragment-screening technique. This approach provides convenient access to a diverse array of cyclohexane and piperidine compounds, prevalent in various bioactive molecules and drugs. Furthermore, it discloses promising avenues for applications in the late-stage switchable saturation of drugs, facilitating an increase in the fraction of sp3-carbons which holds the potential to enhance the medicinal properties of drugs.

EnTdecker - A Machine Learning-Based Platform for Guiding Substrate Discovery in Energy Transfer Catalysis.

Due to the magnitude of chemical space, the discovery of novel substrates in energy transfer (EnT) catalysis remains a daunting task. Experimental and computational strategies to identify compounds that successfully undergo EnT-mediated reactions are limited by their time and cost efficiency. To accelerate the discovery process in EnT catalysis, we herein present the EnTdecker platform, which facilitates the large-scale virtual screening of potential substrates using machine-learning (ML) based predictions of their excited state properties. To achieve this, a data set is created containing more than 34,000 molecules aiming to cover a vast fraction of synthetically relevant compound space for EnT catalysis. Using this data predictive models are trained, and their aptitude for an in-lab application is demonstrated by rediscovering successful substrates from literature as well as experimental validation through luminescence-based screening. By reducing the computational effort needed to obtain excited state properties, the EnTdecker platform represents a tool to efficiently guide substrate selection and increase the experimental success rate for EnT catalysis. Moreover, through an easy-to-use web application, EnTdecker is made publicly accessible under entdecker.uni-muenster.de.

Double Strain-Release [2π+2σ]-Photocycloaddition.

In pursuit of potent pharmaceutical candidates and to further improve their chemical traits, small ring systems can serve as a potential starting point. Small ring units have the additional merit of loaded strain at their core, making them suitable reactants as they can capitalize on this intrinsic driving force. With the introduction of cyclobutenone as a strained precursor to ketene, the photocycloaddition with another strained unit, bicyclo[1.1.0]butane (BCB), enables the reactivity of both π-units in the transient ketene. This double strain-release driven [2π+2σ]-photocycloaddition promotes the synthesis of diverse heterobicyclo[2.1.1]hexane units, a pharmaceutically relevant bioisostere. The effective reactivity under catalyst-free conditions with a high functional group tolerance defines its synthetic utility. Experimental mechanistic studies and density functional theory (DFT) calculations suggest that the [2π+2σ]-photocycloaddition takes place via a triplet mechanism.

EnT-Mediated N-S Bond Homolysis of a Bifunctional Reagent Leading to Aliphatic Sulfonyl Fluorides.

Sulfur(VI) fluoride exchange (SuFEx) gives rise to a plethora of high-valent sulfur linkages; however, the availability of (aliphatic) sulfonyl fluoride manifolds lag behind, owing to the limited sources of introducing the SO2F moiety via a classical two-electron approach. Recently, radical-based methodologies have emerged as a complementary strategy to increase the diversity of accessible click partners. In this work, synthesis of a bench-stable sulfamoyl fluoride reagent is presented, which may undergo sigma-bond homolysis upon visible-light-induced sensitization to form protected ß-amino sulfonyl fluorides from alkene feedstocks. Notably, this offers an appealing strategy to access various building blocks for peptido sulfonyl fluorides, relevant in a medicinal chemistry context, as well as an intriguing entry to ß-ammonium sulfonates and ß-sultams, from alkenes. Densely functionalized 1,3-sultones were obtained by employing allyl alcohols as substrates. Surprisingly, allyl chloride-derived ß-imino sulfonyl fluoride underwent S-O bond formation and ring closure to yield rigid cyclopropyl ß-imino sulfonate ester under SuFEx conditions. Furthermore, by engaging a thiol-based hydrogen atom donor in the reaction, the reactivity of the same reagent can be tuned toward the direct synthesis of aliphatic sulfonyl fluorides. Mechanistic experiments indicate an energy transfer (EnT)-mediated process. The transient sulfonyl fluoride radical adds to the alkene and product formation occurs upon either radical-radical coupling or hydrogen atom transfer (HAT), respectively.

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.