Chromatography–mass spectrometry is a central tool for chemical and biological analysis across a wide range of applications. However, its use in automated workflows has remained limited because instrument operation, chromatogram interpretation and method development have often relied on experienced users. Here, we translate key elements of this analytical expertise into programmable operations, and apply them across workflows of increasing analytical complexity. Most importantly, we show that autonomous, machine-learning-based chromatographic method development can be accelerated substantially by moving beyond black-box optimization and incorporating analytical expertise. We further demonstrate practical use cases from synthetic chemistry and materials discovery, including the automated annotation of complex reaction mixtures, and the autonomous development of a purification method for a target compound from a crude reaction mixture. These capabilities are provided through MoSeS, an open-source Python framework that integrates sample-by-sample instrument control, analyte-resolved data processing, cross-run analyte tracking, and physicochemical retention modelling within a common software layer. Together, these results show how analytical operations that are commonly performed by experts can be formalized into reusable computational workflows. This work provides both a usable software toolbox and practical demonstrations of integrating chromatography–mass spectrometry systems into automated laboratory routines – ranging from custom data analysis pipelines to AI-guided decision making in self-driving laboratories.
Pretrained molecular language models are increasingly used as molecular encoders for learning structure-property relationships. However, their practical suitability for molecular discovery within and beyond their pretraining domain remains unclear. Herein, we systematically benchmark four molecular language models across six virtual molecular libraries spanning drug discovery, organic materials, and catalysis. Native molecular language model embeddings show substantial variation in discovery performance across libraries, whereas molecular fingerprints provide a consistently strong and robust baseline. Consistent with a potential domain-representation mismatch, we show that explicit domain adaptation substantially improves representation performance. Fine-tuning molecular language model encoders on structures from the target virtual library consistently improves sample efficiency, with several adapted encoders emerging as the top-performing representations across the benchmark tasks. These results show that molecular representation quality depends strongly on the target domain and that explicit adaptation can improve the practical utility of molecular foundation models. More broadly, our findings establish domain-adapted molecular representations as a promising strategy for sample-efficient adaptive decision making in virtual screening and self-driving laboratories.
Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a prominent example. While surrogate-based optimization can increase sample efficiency by reducing the number of expensive evaluations, modern molecular libraries have reached billions to trillions of compounds, making full-library surrogate inference itself a major computational bottleneck. We introduce BOBa, a bandit-guided surrogate optimization framework that eliminates full-library inference by adaptively allocating computation across partitions of the action space. By treating partitions as arms in a multi-armed bandit, BOBa concentrates inference and evaluations on empirically promising partitions while maintaining principled exploration. Experiments on real-world synthesis-on-demand libraries demonstrate that optimism-under-uncertainty bandits, combined with meaningful action space partitioning, are essential for effective allocation of inference and evaluations. Our findings reveal a tunable tradeoff between screening performance and surrogate inference cost, which supports practical optimization over current libraries, and establishes a viable route to ultra-large library virtual screening.
General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications such as library synthesis and high-throughput experimentation. However, identifying such conditions efficiently has been a longstanding challenge, as it requires decision making under uncertainty with respect to both conditions and substrates, while minimizing the number of required experiments. Here, we introduce CurryBO, a high-level framework for generality-oriented optimization. By formalizing the problem as Bayesian optimization over curried functions, CurryBO provides a unified framework that accommodates different generality definitions (e.g., mean yield across substrates), and supports a range of substrate and condition selection strategies. We evaluate this framework on four benchmark tasks in experimental reaction optimization, and systematically analyze key algorithmic components. Our results show that efficient experiment planning can be achieved by emphasizing exploration when selecting reaction conditions, followed by the uncertainty-guided prioritization of substrates in a sequential decison-making scheme. Based on these insights, we design and validate an optimization policy that substantially improves sample efficiency relative to previously reported approaches across all benchmarks. Overall, the flexibility and modularity of CurryBO facilitate the integration of generality-oriented optimization into experimental settings, enabling more efficient identification of solutions that perform robustly across diverse tasks.
Scientific optimization problems are usually concerned with balancing multiple competing objectives that express preferences over both the outcomes of an experiment (e.g. maximize reaction yield) and the corresponding input parameters (e.g. minimize the use of an expensive reagent). In practice, operational and economic considerations often establish a hierarchy of these objectives, which must be reflected in algorithms for sample-efficient experiment planning. Herein, we introduce BoTier, a software library that can flexibly represent a hierarchy of preferences over experiment outcomes and input parameters. We provide systematic benchmarks on synthetic and real-life surfaces, demonstrating the robust applicability of BoTier across a number of use cases. Importantly, BoTier is implemented in an auto-differentiable fashion, enabling seamless integration with the BoTorch library, thereby facilitating adoption by the scientific community.
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in molecular discovery, traverse chemical space by performing random mutations and crossovers, leading to a large number of expensive objective evaluations. In this work, we ameliorate this shortcoming by incorporating chemistry-aware Large Language Models (LLMs) into EAs. Namely, we redesign crossover and mutation operations in EAs using LLMs trained on large corpora of chemical information. We perform extensive empirical studies on both commercial and open-source models on multiple tasks involving property optimization, molecular rediscovery, and structure-based drug design, demonstrating that the joint usage of LLMs with EAs yields superior performance over all baseline models across single- and multi-objective settings. We demonstrate that our algorithm improves both the quality of the final solution and convergence speed, thereby reducing the number of required objective evaluations. Our code is publicly available.
Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of high-throughput experimentation, and autonomization of experiment planning and execution, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review article provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research, and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
Bayesian optimization (BO) is an integral part of automated scientific discovery – the so-called self-driving lab – where human inputs are ideally minimal or at least non-blocking. However, scientists often have strong intuition, and thus human feedback is still useful. Nevertheless, prior works in enhancing BO with expert feedback, such as by incorporating it in an offline or online but blocking (arrives at each BO iteration) manner, are incompatible with the spirit of self-driving labs. In this work, we study whether a small amount of randomly arriving expert feedback that is being incorporated in a non-blocking manner can improve a BO campaign. To this end, we run an additional, independent computing thread on top of the BO loop to handle the feedback-gathering process. The gathered feedback is used to learn a Bayesian preference model that can readily be incorporated into the BO thread, to steer its exploration-exploitation process. Experiments on toy and chemistry datasets suggest that even just a few intermittent, asynchronous expert feedback can be useful for improving or constraining BO. This can especially be useful for its implication in improving self-driving labs, e.g. making them more data-efficient and less costly.
Contemporary materials discovery requires intricate sequences of synthesis, formulation and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enables delocalized and asynchronous design–make–test–analyze cycles. We showcase this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based AI experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Automated gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing – and democratizing – scientific discovery.
Self-driving laboratories (SDLs), which combine automated experimental hardware with computational experiment planning, have emerged as powerful tools for accelerating materials discovery. The intrinsic complexity created by their multitude of components requires an effective orchestration platform to ensure the correct operation of diverse experimental setups. Existing orchestration frameworks, however, are either tailored to specific setups or have not been implemented for real-world synthesis. To address these issues, we introduce ChemOS 2.0, an orchestration architecture that efficiently coordinates communication, data exchange, and instruction management among modular laboratory components. By treating the laboratory as an “operating system” ChemOS 2.0 combines ab-initio calculations, experimental orchestration and statistical algorithms to guide closed-loop operations. To demonstrate its capabilities, we showcase ChemOS 2.0 in a case study focused on discovering organic laser molecules. The results confirm the ChemOS 2.0’s prowess in accelerating materials research and demonstrate its potential as a valuable design for future SDL platforms.
Automation is one of the cornerstones of contemporary material discovery. Bayesian optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior domain knowledge into efficient exploration of a large molecular space. While such prior knowledge can take many forms, there has been significant fanfare around the ancillary scientific knowledge encapsulated in large language models (LLMs). However, existing work thus far has only explored LLMs for heuristic materials searches. Indeed, recent work obtains the uncertainty estimate – an integral part of BO – from point-estimated, non-Bayesian LLMs. In this work, we study the question of whether LLMs are actually useful to accelerate principled Bayesian optimization in the molecular space. We take a sober, dispassionate stance in answering this question. This is done by carefully (i) viewing LLMs as fixed feature extractors for standard but principled BO surrogate models and by (ii) leveraging parameter-efficient finetuning methods and Bayesian neural networks to obtain the posterior of the LLM surrogate. Our extensive experiments with real-world chemistry problems show that LLMs can be useful for BO over molecules, but only if they have been pretrained or finetuned with domain-specific data.
Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of “hybrid” algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of synthetic chemistry, we advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts. By actively involving synthetic chemists, who are the end users of any synthesis planning software, into the development process, we envision to bridge the gap between computer algorithms and the intricate nature of chemical synthesis.
We introduce Chemspyd, a lightweight, open-source Python package for operating the popular laboratory robotic platforms from Chemspeed Technologies. As an add-on to the existing proprietary software suite, Chemspyd enables dynamic communication with the automated platform, laying the foundation for its modular integration into customizable, higher-level laboratory workflows. We show the applicability of Chemspyd in a set of case studies from chemistry and materials science. We demonstrate how the package can be used with large language models to provide a natural language interface. By providing an open-source software interface for a commercial robotic platform, we hope to inspire the development of open interfaces that facilitate the flexible, adaptive integration of existing laboratory equipment into automated laboratories.
Excited (triplet) states offer a myriad of attractive synthetic pathways, including cycloadditions, selective homolytic bond cleavages and strain-release chemistry, isomerizations, deracemizations, or the fusion with metal catalysis. Recent years have seen enormous advantages in enabling these reactivity modes through visible-light-mediated triplet–triplet energy transfer catalysis (TTEnT). This tutorial review provides an overview of this emerging strategy for synthesizing sought-after organic motifs in a mild, selective, and sustainable manner. Building on the photophysical foundations of energy transfer, this review also discusses catalyst design, as well as the challenges and opportunities of energy transfer catalysis.
We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to molecular representations, however, necessitates kernels defined over structured inputs such as graphs, strings and bit vectors. By providing such kernels in a modular, robust and easy-to-use framework, we seek to enable expert chemists and materials scientists to make use of state-of-the-art black-box optimization techniques. Motivated by scenarios frequently encountered in practice, we showcase applications for GAUCHE in molecular discovery, chemical reaction optimisation and protein design. The codebase is made available at https://github.com/leojklarner/gauche.
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness. SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
Providing strong carbon-metal (C–M) bonds, N-heterocyclic carbenes (NHCs) recently were introduced as surface modifiers for various applications. Here we report a fluorinated NHC (F-NHC) monolayer chemically bonded to Au with high thermal stability up to 200 °C. The effect of F-NHC modification on contact resistance in organic field-effect transistors (OFETs) was further investigated. In comparison to devices without F-NHC modification, transistors with the F-NHC modified gold electrodes show a significant improvement in carrier mobility in the saturation region owing to the reduced contact resistance (Rc). Density Functional Theory (DFT) calculations indicate that, when bonding to Au, the F-NHC molecule transfers 0.40 e to Au and creates a doping interface between Au and p-type organic semiconductors. The charge transfer between the F-NHC and Au leads to a generation of holes at the F-NHC/organic semiconductor interface, which may cause an efficient carrier tunneling for contact resistance improvement. The work lays the foundation for the future applications of carbenes in organic electronics.
Assessing the outcomes of chemical reactions in a quantitative fashion has been a cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data-driven modelling bears an enormous potential to streamline this process. However, such predictive models require significant quantities of high-quality data, the availability of which is limited. Main reasons for this include experimental errors and, importantly, human biases regarding experiment selection and result reporting. In a series of case studies, we investigate the impact of these biases for drawing general conclusions from chemical reaction data, revealing the utmost importance of “negative” examples. Eventually, case studies into data expansion approaches showcase directions to circumvent these limitations—and demonstrate perspectives towards a long-term data quality enhancement in chemistry.
A photocatalyzed ring expansion procedure for a two-carbon homologation of cyclic carbonyl compounds, towards medium-sized ring systems, is introduced. Key to success is the catalyst fac-[Ir(CF3-pmb)3] (ET=73.3 kcal mol−1), which allows for selective activation of the substoichiometric enol species. The mechanism has been corroborated by time-resolved spectroscopy and enabled a full kinetic analysis of the dynamic kinetic sensitization scenario.
The development of a catalytic strategy for one-carbon homologation is of foundational importance in organic synthesis enabling quick elaboration of useful molecules. Herein, we report a dual nickel/photoredox catalytic strategy for transforming a range of α-amino acids into β-amino aldehydes in a simple one-step operation. This method provides a new catalytic blueprint for one-carbon homologation of α-amino acids without requiring any pre-functionalization step and could be strategically important for streamlining the preparation of value-added β-amino aldehydes, versatile building blocks applied in a variety of synthetic transformations. More broadly, mechanistic studies demonstrated here show how a radical generated in situ can be captured for rapid access of aldehydes, which would promote the applications of numerous other molecules for valuable aldehyde synthesis.
The development of organic materials with high solid-state luminescence efficiency is highly desirable because of their fundamental importance and applicability in optoelectronics. Herein, a rapid construction of novel BF2 complexes with N,O-bidentate ligands by using Cu(BF4)2•6H2O as a catalyst and BF2 source is disclosed, which avoids the need for pre-composing the N,O-bidentate ligands and features a broad substrate scope and a high tolerance level for sensitive functional groups. Moreover, molecular oxygen is employed as the terminal oxidant in this transformation. A library of 36 compounds as a new class of BF2 complexes with remarkable photophysical properties is delivered in good to excellent yields, showing a substituent-dependency on the photophysical properties, derived from the π–π* character of the photoexcited state. In addition, aggregation-induced emission (AIE) is observed and quantified for the brightest exemplars. The excited state properties are fully investigated in solids and in THF/H2O mixtures. Hence, a new series of photofunctional materials with variable photophysical properties is reported, with potential applications for sensing, bioimaging, and optoelectronics.
Energy transfer can leverage the enormous potential of excited-state reactivity. Through “indirect excitation” of substrates, otherwise elusive reactivity modes can be switched on, allowing for, e.g., cycloadditions, fragmentations, rearrangements, or challenging organometallic steps. This perspective recaps almost 70 years of energy transfer in organic chemistry, highlighting the way it evolved, as well as recent developments in the field of visible-light photocatalysis. Building upon the photophysical fundamentals, diverse applications and directions of energy transfer catalysis are pointed out.
Machine learning (ML) has emerged as a general, problem-solving paradigm with many applications in computer vision, natural language processing, digital safety, or medicine. By recognizing complex patterns in data, ML bears the potential to modernise the way how many chemical challenges are approached. In this review, an introduction to ML is given from the perspective of synthetic chemistry. Starting from the fundamentals regarding algorithms and best-practice workflows, the review covers different applications of machine learning in synthesis planning, property prediction, molecular design, and reactivity prediction. In particular, different approaches of representing and utilizing organic molecules will be discussed – providing synthetic chemists both with the understanding and the tools required to apply machine learning in the context of their research, and pointers for further studying.
Organic BF2 complexes exhibit characteristics such as large Stokes shift, high quantum yield, strong emission intensity, and robust chemical stability, thereby being extensively used in various applications. Herein, we disclose a novel copper-catalyzed cascade C−H activation/acyloxylation and difluoroboronation of 2-phenylpyridine derivatives, thus providing a straightforward and rapid gateway to a series of N,O-bidentate organic BF2 complexes with excellent photophysical properties. Mechanism studies demonstrate that AgBF4 services as BF2 source and oxidant for this elegant transformation. Most of these BF2 complexes have broad and intense absorption and emission bands, and display bright and intensive blue fluorescence as well as large Stokes shifts.
Gaining an understanding of the conformational behavior of fluorinated compounds would allow for expansion of the current molecular design toolbox. In order to facilitate drug discovery efforts, a systematic survey of a series of diversely substituted and protected fluorinated piperidine derivatives has been carried out using NMR spectroscopy. Computational investigations reveal that, in addition to established delocalization forces such as charge–dipole interactions and hyperconjugation, solvation and solvent polarity play a major role. This work codifies a new design principle for conformationally rigid molecular scaffolds.
Despite their enormous potential, machine learning methods have only found limited application in predicting reaction outcomes, because current models are often highly complex and, most importantly, are not transferable to different problem sets. Here, we present a structure-based machine learning platform for diverse applications in organic chemistry. Therefore, an input based on multiple fingerprint features (MFFs) as a versatile molecular representation was developed that was shown to be applicable over a range of diverse problem sets. First, molecular properties across a diverse array of molecules could be predicted accurately. Next, reaction outcomes such as stereoselectivities and yields were predicted for experimental datasets that were previously evaluated using (complex) problem-oriented descriptor models. As a final application, a systematic high-throughput dataset was investigated as a “real-world problem,” and good correlation was observed when using the structure-based model.
An intermolecular, two-component vicinal carboimination of alkenes has been accomplished by energy transfer catalysis. Oxime esters of alkyl carboxylic acids were used as bifunctional reagents to generate both alkyl and iminyl radicals. Subsequently, addition of the alkyl radical to an alkene generates a transient radical for selective radical–radical cross-coupling with the persistent iminyl radical. Furthermore, this process provides direct access to aliphatic primary amines and α-amino acids by simple hydrolysis.
Investigations into the selectivity of intermolecular alkyl radical additions to C–O- vs. C–C-double bonds in α,β-unsaturated carbonyl compounds are described. Therefore, a photoredox-initiated radical chain reaction is explored, where the activation of the carbonyl-group through an in situ generated Lewis acid – originating from the substrate – enables the formation of either C–O or the C–C-addition products. α,β-Unsaturated aldehydes form selectively 1,2-, while esters and ketones form the corresponding 1,4-addition products exclusively. Computational studies lead to reason that this chemo- and regioselectivity is determined by the consecutive step, i.e. an electron transfer, after reversible radical addition, which eventually propagates the radical chain.
The catalytic dearomatization of pyridines, accessing medicinally relevant N-heterocycles, is of high interest. Currently direct, dearomative strategies rely generally on reduction or nucleophilic addition, thus limiting the architecture of the dearomatized products to a six-membered ring. We herein introduce a catalytic, dearomative cycloaddition reaction with pyridines using photoinduced energy transfer catalysis, thereby advancing dearomatization methodology and increasing the topology of pyridine dearomatization products. This unprecedented method features high yields, broad substrate scope (44 examples), excellent functional group tolerance, and facile scalability. Furthermore, a recyclable and sustainable polymer immobilized photocatalyst was employed. Computational and experimental investigations support a mechanism in which a cinnamyl moiety is promoted to its corresponding excited triplet state through visible-light-mediated energy transfer catalysis, followed by a regioselective and dearomative [4+2] cycloaddition to pyridines. This work demonstrates the contribution of visible light catalysis toward enabling thermally challenging organic transformations.
The discovery of novel (catalytic) transformations and mechanisms is commonly based on rational design. However, many discoveries have resulted directly from experimental serendipity. Building on this, we report a two-dimensional screening protocol, combining “mechanism-based” and “reaction-based” screening and its application to the field of visible light photocatalysis. To this end, two energy-transfer-based cycloaddition reactions could be realized. A notably endergonic energy transfer process allows for the dearomative cycloaddition of benzothiophenes and related heterocycles. Moreover, by sensitization of enone moieties, a [2+2]-cycloaddition to alkynes and an unexpected cycloaddition-rearrangement cascade were discovered. Advanced spectroscopic techniques (in particular transient absorption spectroscopy and pulse radiolysis) were utilized to investigate the underlying photophysical processes and gain insight into reaction kinetics. Combining these results with further mechanistic analysis can eventually turn out to be helpful upon knowledge-driven development of improved systems. Such screening approaches can thus provide complementary access toward novel and more efficient catalytic protocols.
Despite significant progress in aliphatic decarboxylation, an efficient and general protocol for radical aromatic decarboxylation has lagged far behind. Herein, we describe a general strategy for rapid access to both aryl and alkyl radicals by photosensitized decarboxylation of the corresponding carboxylic acids esters followed by their successive use in divergent carbon–heteroatom and carbon–carbon bond-forming reactions. Identification of a suitable activator for carboxylic acids is the key to bypass a competing single-electron-transfer mechanism and “switch on” an energy-transfer-mediated homolysis of unsymmetrical σ-bonds for a concerted fragmentation/decarboxylation process.
The development and application of traceless acceptor groups in photochemical C−C bond formation is described. This strategy was enabled by the photoexcitation of electron donor–acceptor (EDA) complexes with visible light. The traceless acceptors, which were readily prepared from amino acid and peptide feedstocks, could be used to alkylate a wide range of heteroarene and enamine donors under metal- and peroxide-free conditions. The crucial role of the EDA complexes in the mechanism of these reactions was explored through combined experimental and computational studies.
A deaminative strategy for the borylation of aliphatic primary amines is described. Alkyl radicals derived from the single-electron reduction of redox-active pyridinium salts, which can be isolated or generated in situ, were borylated in a visible light-mediated reaction with bis(catecholato)diboron. No catalyst or further additives were required. The key electron donor–acceptor complex was characterized in detail by both experimental and computational investigations. The synthetic potential of this mild protocol was demonstrated through the late-stage functionalization of natural products and drug molecules.
A visible-light-mediated approach to carbonyl–olefin cross-metathesis is described. Photoinduced hole catalysis was used to promote the formation of 1,3-diols from aldehydes and styrenes, which were then readily fragmented under acidic conditions to form the cross-metathesis products. The use of 1,3-diols as intermediates, rather than the energetically more demanding oxetanes, provides a new, orthogonal mechanistic strategy for carbonyl–olefin cross-metathesis. Furthermore, this approach does not require any metals, ligands, or additives, and provides the products with high levels of E selectivity. A mechanistic rationale is provided and supported by both theoretical calculations and experiments. Additionally, a practical synthesis of a new acridinium-based photocatalyst, including full characterization, is presented.
Harnessing visible light to access excited (triplet) states of organic compounds can enable impressive reactivity modes. This tutorial review covers the photophysical fundamentals and most significant advances in the field of visible-light-mediated energy transfer catalysis within the last decade. Methods to determine excited triplet state energies and to characterize the underlying Dexter energy transfer are discussed. Synthetic applications of this field, divided into four main categories (cyclization reactions, double bond isomerizations, bond dissociations and sensitization of metal complexes), are also examined.
Sulfur-containing molecules participate in many essential biological processes. Of utmost importance is the methylthioether moiety, present in the proteinogenic amino acid methionine and installed in tRNA by radical-S-adenosylmethionine methylthiotransferases. Although the thiol–ene reaction for carbon–sulfur bond formation has found widespread applications in materials or medicinal science, a biocompatible chemo- and regioselective hydrothiolation of unactivated alkenes and alkynes remains elusive. Here, we describe the design of a general chemoselective anti-Markovnikov hydroalkyl/aryl thiolation of alkenes and alkynes—also allowing the biologically important hydromethylthiolation—by triplet–triplet energy transfer activation of disulfides. This fast disulfide–ene reaction shows extraordinary functional group tolerance and biocompatibility. Transient absorption spectroscopy was used to study the sensitization process in detail. The hereby gained mechanistic insights were successfully employed for optimization of the catalytic system. This photosensitized transformation should stimulate bioimaging applications and carbon–sulfur bond-forming late-stage functionalization chemistry, especially in the context of metabolic labelling.
The discovery and application of dearomative cascade photocatalysis as a strategy in complex molecule synthesis is described. Visible-light-absorbing photosensitizers were used to (sequentially) activate a 1-naphthol derived arene precursor to divergently form two different polycyclic molecular scaffolds through catalyst selective energy transfer.
Herein, we report a novel strategy for the activation of DMSO to act as a versatile alkylating agent in heteroarene C−H functionalization. This direct, simple, and mild switch between methylation/trideuteromethylation and methylthiomethylation of heteroarenes was achieved under reagent-controlled photoredox catalysis conditions. The proposed mechanism is supported by both experimental and computational studies.
Herein, we introduce a new class of bench-stable N-heterocyclic carbene (NHC) nickel-precatalysts for homogeneous nickel-catalysis. The nickel(II) complexes are readily activated to Ni0 in situ under mild conditions, via a proposed Heck-type mechanism. The precatalysts are shown to facilitate carbonyl-ene, hydroalkenylation, and amination reactions.
Herein, we present a novel strategy for the utilization of simple carbonyl compounds, aldehydes and ketones, as intermolecular radical acceptors. The reaction is enabled by visible light photoredox initiated hole catalysis and the in situ Brønsted acid activation of the carbonyl compound. This regioselective alkyl radical addition reaction does not require metals, ligands or additives and proceeds with a high degree of atom economy under mild conditions. The proposed mechanism is supported by both experimental and theoretical studies.