Few elements exemplify the duality of chemistry-where utility and toxicity depend on application-as strikingly as arsenic. Throughout history, arsenic has played a pivotal role in human civilization, and its influence endures in modern science through organoarsenicals, compounds featuring As-C bonds. However, the synthesis of these molecules has long been constrained by multistep processes reliant on hazardous arsenical reagents, limiting their practical adoption. Here we show a transformative mineral-to-molecule approach that leverages photoredox catalysis to directly convert naturally occurring arsenic sulfide minerals-particularly orpiment (As2S3)-into diverse organoarsenicals under visible-light irradiation. By reacting mineral-derived arsenic with organic iodides, our method bypasses toxic intermediates, enabling the efficient, one-step construction of functionalized arsenicals with broad structural diversity. Demonstrated across a wide range of substrates, this strategy establishes a safe, sustainable and scalable route to organoarsenicals, overcoming long-standing challenges in arsenic chemistry. Bridging geochemistry and synthetic chemistry, our work provides a blueprint for eco-friendly molecular manufacturing, transforming Earth's mineral resources into high-value functional molecules.

Highly strained rings serve as privileged building blocks for the synthesis of saturated, three-dimensional scaffolds, which are increasingly recognized as critical components in modern drug discovery. Here we disclose a substrate-dependent, divergent strategy to access a broad family of housanes through an intramolecular-energy-transfer-mediated [2 + 2] cycloaddition of 1,4-dienes-a transformation that has long been considered challenging. This method rapidly builds up strain while suppressing the di-pi-methane rearrangement, thereby expanding the toolkit for efficient exploration of housane chemical space. Substituent engineering enables switching between single and double energy-transfer pathways to deliver 1,3- and 1,2-disubstituted housanes with excellent stereocontrol and broad functional-group tolerance. Mechanistic studies and density functional theory calculations support an energy-transfer pathway and rationalize the observed selectivity.

N-Heterocyclic carbene (NHC) self-assembled monolayers (SAMs) are emerging as robust and versatile surface ligands, yet their direct on-surface construction with atomic precision remains elusive. Here, we report a reagent-free, thermally driven on-surface strategy for single-atom editing, providing an alternative to convert a heteroatom-anchored SAM into a functional NHC layer. N-Heterocyclic thiourea (NHT) with carefully chosen substituents undergoes a S-to-metal adatom exchange, yielding adatom-bound NHC on coinage metal surfaces. Using low-temperature scanning tunneling microscopy (LT-STM), synchrotron-radiation X-ray photoelectron spectroscopy (SR-XPS), time of flight secondary ion mass spectrometry (ToF-SIMS) and density functional theory (DFT), we identify two distinct classes of SAMs: (i) NHT-SAM, in which sulfur coordinates to the metal surface and (ii) NHC-SAM, characterized by M─C σ-bonds between the carbene center and a surface adatom. NHT undergoes a clean SAM-to-SAM conversion, achieving covalent single-atom editing within an intact monolayer. DFT calculations show this transformation is energetically favorable and highly tunable by substrates and N-substituents, establishing a general framework for atom-precise molecular editing at surfaces.

Partially saturated heteroarenes are key intermediates in drug development, as their increased C(sp3) character and inherent synthetic versatility enable the rapid generation of improved lead structures. Despite their value, direct access from their aromatic precursors under Birch conditions remains limited due to safety, as well as chemoselectivity challenges. In this context, energy-transfer (EnT)-enabled Birch-type reductions have emerged as a powerful strategy for (chemo)selective product formation. However, it has been largely confined to bicyclic (hetero)arenes, while their more prevalent monocyclic counterparts remain vastly underexplored. Herein, we describe the triplet sensitized, partial saturation of 2-pyridones and their derivatives, which are known as privileged scaffolds in drug discovery. Our methodology shows exceptional regioselectivity for the Birch-type product and tolerates a vast number of functional groups, bypassing conventional highly reducing reaction conditions. The reaction mechanism was investigated experimentally and computationally, providing strong evidence for the proposed EnT-HAT reaction pathway and rationalizing the observed regioselectivity. Lastly, the versatile and underexplored product motifs could be applied in downstream modifications to expand the drug-like C(sp3)-rich chemical space.

Medium-sized bridged heterocycles are highly attractive structural motifs in bioactive natural products and medicinal chemistry. However, their broader exploration remains limited due to the lack of concise and modular synthetic strategies, as their synthesis is challenged by unfavorable enthalpic and entropic constraints. Herein, we report an energy transfer (EnT)-catalyzed intermolecular (5 + 4) dearomative cycloaddition of furans with vinyl cyclopropanes, providing direct access to (Z)-10-oxabicyclo[5.2.1]deca-3,8-diene scaffolds in a single step. Visible light triplet sensitization unlocks a distinct diene-type 1,4-biradical activation mode in furans. This triplet-state reactivity enables intermolecular higher-order (5 + 4) cycloadditions that remain inaccessible under conventional thermal activation. Mechanistic investigations support an energy transfer pathway and provide insights into the origin of the observed regioselectivity. The resulting partially unsaturated cycloadducts offer multiple synthetic handles for downstream functionalization, enabling rapid and modular incorporation of heterobicyclo[5.2.1]alkane motifs and highlighting their potential as versatile building blocks in synthetic chemistry.

The cell membrane is a prime target for the introduction of novel cellular functionalities, as it is a complex system with many routes for surface modification. Several chemical coating and genetic engineering methods have thus been developed for this purpose. Here, a distinct way to enable enzyme-binding onto the surface of bacterial cells is explored using biomimetic lipids that integrate within the cell membrane. E. coli cells were equipped with a cholesterol-based artificial lipid containing a nitrilotriacetic acid (NTA) group which, when loaded with Ni2+ ions, selectively binds His-tagged enzymes through affinity interactions. This interaction is stable and selective for tagged proteins including green fluorescent protein, enabling their direct one-step purification and immobilisation from cell lysates. Furthermore, the process is biocompatible and preserves both intracellular and cell-surface enzymatic activity. This strategy further enables binding of benzaldehyde lyase or amine transaminase enzymes to the surface of bacterial cells for recyclable single-step enzymatic reactions. Importantly, it allowed the creation of a single-cell system for the two-step cascade reaction from benzyl alcohol to (R)-benzoin using both intracellular and surface-immobilised enzymes. This provides a solid proof of concept for the streamlined development of cascade reaction systems in a single cell through non-genetic cell surface enzyme immobilisation.

Densely substituted heterocycles are ubiquitous motifs in both natural products and pharmaceuticals. sp3-Heterocycles bearing multiple vicinal stereocenters are especially critical targets due to their challenging construction under precise stereocontrol. The multisubstituted tetrahydrofuran (THF) family, for example, represents a privileged structural motif. Yet, syntheses of THFs are often step-intensive and tailored to a single target structure, as generalized and robust methods that rapidly introduce multiple substituents from simple precursors remain underdeveloped. Herein, we present a distinct approach featuring two core advantages: modular assembly from abundant building blocks, and precise chemo-, regio-, diastereo-, and enantioselectivity. Pivotal to the success of this methodology is synergistic metallaphotoredox-Lewis acid catalysis, which rapidly constructs four bonds in THFs bearing up to four stereocenters. Experimental and computational studies deliver further insight into the mechanism. High-throughput experimentation (HTE) highlights the potential for efficient generation of THF-based libraries.

Nitrogen-enriched (partially) saturated fused heterocycles have emerged as highly relevant scaffolds for improved pharmaceuticals. Increased solubility, along with fine-tuneable target affinity and specificity, differentiate them from their nitrogen-poor aromatic counterparts. Contrary to their growing demand, applications are severely limited by arduous bottom-up synthesis routes and the lack of a general solution for facile access. Herein, we report an efficient method for the synthesis of chiral (semi-)saturated pyridine-fused heterocycles and their respective N-permutations by enantioselective arene hydrogenation with a newly developed ruthenium catalyst. We obtained versatile and highly valuable product motifs, including pyridine- and piperidine-fused scaffolds with up to four newly formed stereocenters, of which several have not been previously reported. We conducted extensive in silico studies to elucidate a rare inverse-pressure-dependent enantioselectivity and to develop a rational model for predicting the stereochemical outcome. This contribution is expected to accelerate the exploration of new frameworks in drug discovery.

Agonists of the κ-opioid receptor are useful drugs for the treatment of severe pain, itching skin diseases and inflammatory and immunological diseases. Herein, novel κ agonists with the κ-pharmacophoric ethylenediamine system embedded in a rigid decahydroquinoline scaffold (6) were designed, synthesized and pharmacologically evaluated. The synthesis of decahydroquinolines 6 consisted of three parts: (1) synthesis of 4,8-disubstituted tetrahydroquinolines 14; (2) diastereoselective hydrogenation of tetrahydroquinolines 14 to afford decahydroquinolines 17; and (3) stereoselective introduction of the pyrrolidine ring at the 8-position and various acyl moieties at the 1-position. The dichlorophenylacetyl and fluorophenylacetyl derivatives 6a (Ki = 86 nM) and 6b (Ki = 134 nM) showed considerably lower κ affinity than the lead compounds 4 (Ki = 0.81 nM) and 5 (Ki = 0.25 nM). In docking studies, the NH moiety of the exocyclic carbamates 6a and 6b served as an H-bond donor towards the OH moiety of Y239, whereas the methoxycarbonyl moiety of endocyclic carbamate 5 formed a beneficial H-bond with the NH backbone of L212. The lower κ affinity of 6a and 6b was at least partially compensated by increased polarity, leading to promising LLE values of 5.69 and 6.87, respectively. Both κ agonists 6a and 6b revealed high selectivity over µ- and δ-opioid receptors and high metabolic stability in the presence of mouse liver microsomes and NADPH. The anti-inflammatory activity of the κ receptor agonist 6a was investigated with human peripheral blood mononuclear cells stimulated with lipopolysaccharide, and the effects were compared with those of the lead compounds 4 and 5. Methyl carbamate 6a exhibited the smallest reduction in pro-inflammatory monocyte subsets and did not affect cytokine secretion. It was concluded that 6a had a substantially weaker anti-inflammatory activity than the lead compounds 4 and 5.

Since Graph Neural Networks (GNNs) made a big impact on graph structured datasets, they are widely utilized in the field of chemistry. However, the reasons behind the prediction of GNNs are not always obvious, so they are considered as black-box models. In this paper, we introduce a graphical user interface (GUI) which can be used for explaining the predictions of GNNs. We aim to integrate our GUI into the user’s research directly to make the predictions of GNNs more understandable in both classification and regression tasks. Furthermore, we offer the option to use the built-in GNN models to train custom datasets directly. Additionally, the system incorporates several explainable artificial intelligence (XAI) techniques, and also allows users to assess the accuracy of explanation findings using various assessment metrics and thus to compare the explanation outcomes. Using the well-known datasets in the field, this tool can also be used for education purposes. The interface provides a comprehensive platform for examining and interpreting the predictions provided by the GNNs and merging several GNN models with XAI approaches. This will facilitate a deeper understanding and possibly lead to new discoveries in researchers’ respective domains in understanding the underlying elements that influence the model’s explainability. The code is made publicly available at https://github.com/ChemGraphExplainer/ChemGraphExplainer .

It is well established that transport of electrons through chiral molecules can depend on their spins. However, it is not yet comprehensible how to relate molecular structure and size to spin selectivity. N-heterocyclic carbenes (NHCs) are a unique class of small organic molecules with electronic tunability generating huge structural and applicative diversity and covalently bind to metal surfaces to form a robust self-assembled monolayer (SAM). Here, we demonstrate that this family of molecules exhibits high spin selectivity, despite their small size, while being extremely stable on metal surfaces. We have fabricated chiral NHC thin films on the metal surfaces and utilized them as electron spin filters for the first time. The (S,S)-SINpEt and (R,R)-SINpEt thin films on Au-Ni surfaces showcase a high electronic spin polarization (SP) of up to 60%. The chiral NHC thin films exhibit electrochemical stability under continuous voltametric interrogation for 50 cycles (within a -0.3 to 0.3 V vs Ag/AgCl voltage window) in an aqueous solution (pH = 13), which enables the utilization of spin-polarized electrons to enhance the oxygen reduction reaction (ORR). The ORR with chiral SINpEt thin films of different thicknesses indicates the importance of the coherency of the electrons in the ORR reaction. The binding, orientation of the chiral SINpEt molecules, and their molecular coverage on the Au surface have also been investigated using quantum chemical calculations, which ensure a good agreement with the experimental observations. This finding introduces a new class of small chiral molecules as a useful source of electronic spin-polarization for various applications.

Li-Ion Batteries Si-based anodes can increase the energy density of Li ion batteries but require re-formation of solid electrolyte interphase (SEI) via reduction reactions, which consumes active Li, thus capacity. Interestingly, some apparently "dead" SEI species can cross over to the cathode and recover the apparently lost active Li via oxidation reactions. In article number e202500637, Johannes Kasnatscheew and co-workers.

Densely substituted heterocycles are ubiquitous motifs in both natural products and pharmaceuticals. sp3-Heterocycles bearing multiple vicinal stereocenters are especially critical targets due to their challenging construction under precise stereocontrol. The multisubstituted tetrahydrofuran (THF) family, for example, represents a privileged structural motif. Yet, syntheses of THFs are often step-intensive and tailored to a single target structure, as generalized and robust methods that rapidly introduce multiple substituents from simple precursors remain underdeveloped. Herein, we present a distinct approach featuring two core advantages: modular assembly from abundant building blocks, and precise chemo-, regio-, diastereo-, and enantioselectivity. Pivotal to the success of this methodology is synergistic metallaphotoredox-Lewis acid catalysis, which rapidly constructs four bonds in THFs bearing up to four stereocenters. Experimental and computational studies deliver further insight into the mechanism. High-throughput experimentation (HTE) highlights the potential for efficient generation of THF-based libraries.

The vast reaction data within scientific literature represents a rich resource for training predictive machine learning models. However, this resource is fundamentally compromised by a pervasive selection and reporting bias, resulting in imbalanced data sets. In this work, we introduce "Positivity is All You Need" (PAYN), a machine learning framework that addresses this data-scarcity problem by learning directly from biased, positive-only data. PAYN leverages a spy-based positive-unlabeled (PU) learning strategy, treating reported high-yielding reactions as the "positive" class and the vast, unexplored chemical space as the "unlabeled" class. To validate our approach, we simulated literature bias on fully labeled high-throughput experimentation (HTE) data sets, including Ni-catalyzed borylations, Buchwald-Hartwig and Suzuki-Miyaura couplings. We demonstrated that PAYN significantly improves the performance of models trained on biased data by balancing the data with augmented negative data points. This work establishes a robust strategy for leveraging biased data, paving a path toward more scalable and accessible data-driven strategies for accelerating synthesis design, optimization, and chemical discovery.

ABSTRACT We report a robust redox‐active N‐heterocyclic carbene (NHC) monolayer that exhibits synapse‐like behavior driven by proton‐coupled electron transfer (PCET). Our quinone‐functionalized NHC (Rex–NHC) forms densely packed, upright self‐assembled monolayers (SAMs) on Au, confirmed by cyclic voltammetry, x‐ ray photoelectron spectroscopy, sum‐frequency generation spectroscopy, and infrared reflection absorption spectroscopy. Molecular junctions built as Au–Rex–NHC//Ga 2 O 3 /EGaIn operate over ± 2 V and can withstand electric fields up to 3.3 GV/m. Bias‐induced PCET toggles between quinone (off) and hydroquinone (on) states, yielding reversible hysteresis with on/off ratios up to 1.9 × 10 2 . The devices exhibit spike‐timing and spike‐rate‐dependent plasticity, demonstrating for the first time molecular‐level neuromorphic behavior using NHCs as anchoring groups.

The discovery of new organic photocatalysts (PCs) for energy transfer (EnT) catalysis remains a significant challenge, largely due to the vast and underexplored chemical space and the delicate balance of the photocatalytic properties. While transition-metal catalysts are effective, their high cost and environmental impact necessitate the development of metal-free alternatives. In this work, we present a hybrid inverse molecular design strategy that combines global exploration with targeted local optimization to discover highly efficient organic PCs. Our approach leverages a generative model, guided by machine learning predictions and semiempirical simulations, to efficiently navigate chemical space and identify promising molecular scaffolds. We demonstrate the utility of this strategy by rediscovering known PCs and, more importantly, exploring uncharted structural regions, leading to the identification of novel candidates with favorable photophysical properties. A subsequent local exploration stage, using quantum mechanical calculations, allows refinement of the properties as well as control of the synthetic complexity. The practical applicability of the approach is demonstrated by performing a local exploration of one of the identified scaffolds and successfully synthesizing four candidate PCs. We showcase their catalytic aptitude in three different EnT-mediated reactions, including a challenging aza-photocycloaddition, where one of our designed PCs achieved 90% yield, a performance comparable to a state-of-the-art iridium-based catalyst. This study highlights the power of a data-driven inverse design framework to bridge computational discovery and experimental validation, accelerating the identification of novel PCs and expanding the scope of EnT catalysis.

ABSTRACT The growing integration of artificial intelligence (AI) and machine learning (ML) is transforming experimental chemistry laboratories. Especially in synthetic chemistry, researchers routinely handle complex and high‐dimensional data, fostering meaningful synergies between chemistry and data science. This review is intended as a practical overview that connects the everyday challenges of synthetic chemists with the digital tools available to address them. It does not seek to explain theoretical foundations of ML or to provide a comprehensive survey of all recent studies in the field. Rather, our goal is to highlight emerging technologies, discuss key considerations for their application, and present a selection of illustrative examples. To begin, we outline the prerequisites for successfully applying data science in synthetic chemistry. Next, we give a realistic overview of strategies and bottlenecks in predictive modeling of molecular properties, reaction outcomes and reaction conditions. We further highlight data‐driven approaches that can be applied in the development of new chemical reactions and synthetic methodologies, including all relevant stages from reaction discovery and optimization to substrate scope evaluation and mechanistic analyses. Finally, we briefly discuss the transformative role of large language models and agentic workflows in synthetic chemistry, focusing on opportunities and challenges in the laboratories of the future.

N-Heterocyclic carbenes (NHCs) have recently emerged as the next-generation surface ligands with improved stability and molecular flexibility. Despite these premises, research on NHC-enriched flat surfaces is mainly limited to noble metals, while formation of free NHCs often requires the use of vacuum, bases, or strictly air- and moisture-free conditions. We hereby report an unprecedented radical-to-carbene-based approach for fabricating NHC monolayers on earth-abundant and naturally oxidized metal surfaces of nickel, iron, and stainless steel. Following an open-cell electrografting approach, 2-azolyl radicals are firstly formed and immobilized on the metal to then rearrange into NHC monolayers apparently composed of flat-lying NHCs, as corroborated by X-ray photoelectron spectroscopy (XPS), time-of-flight secondary ion mass spectrometry (ToF-SIMS), sum-frequency generation (SFG), and cyclic voltammetry (CV) measurements. Density functional theory (DFT) calculations highlighted the role of metal adatoms in facilitating the radical-to-carbene transition. A surface stability test was conducted to assess the tolerance of the NHC-enriched surfaces toward physical, chemical, and electrochemical stress. Ultimately, this work expands the application field of carbenes-on-surfaces to cost-effective and widely used materials, while offering an agile and, until now, mechanistically unknown approach to their generation.