Microwave-enhanced additive-free C–H amination of benzoxazoles catalysed by supported copper

• Andrei Paraschiv,
• Valentina Maruzzo,
• Filippo Pettazzi,
• Stefano Magliocco,
• Paolo Inaudi,
• Daria Brambilla,
• Gloria Berlier,
• Giancarlo Cravotto and
• Katia Martina

Beilstein J. Org. Chem. 2025, 21, 1462–1476, doi:10.3762/bjoc.21.108

• ) catalyst reuse. FESEM images of sample a) Si-MonoAm-Cu(I) 5 wt % and c) Si-MonoAm-Cu(I) 5 wt % used. EDS maps of a) Si-MonoAm-Cu(I) and b) Si-MonoAm-Cu(I) used. Representative synthetic routes for the C–H amination of benzoxazole using supported copper catalysts. Graphical representation of Si-MonoAm-Cu(I

High-pressure activation for the solvent- and catalyst-free syntheses of heterocycles, pharmaceuticals and esters

• Kelsey Plasse,
• Valerie Wright,
• Guoshu Xie,
• R. Bernadett Vlocskó,
• Alexander Lazarev and
• Béla Török

Beilstein J. Org. Chem. 2025, 21, 1374–1387, doi:10.3762/bjoc.21.102

• pressure at small (≈150–200 mg) scale reactions. Schematic representation of the cycling experiments: the major variables are the applied pressure, the holding time, the length of decompression and the number of cycles in the sequence. High pressure-initiated synthesis of 1,3-dihydrobenzimidazoles 3a–d

Enhancing chemical synthesis planning: automated quantum mechanics-based regioselectivity prediction for C–H activation with directing groups

• Julius Seumer,
• Nicolai Ree and
• Jan H. Jensen

Beilstein J. Org. Chem. 2025, 21, 1171–1182, doi:10.3762/bjoc.21.94

• representation is compared to all other SMILES with an added dummy atom. 5. Duplicates are removed when the directing group has equivalent resonance forms, as shown in Figure 8. The equivalent heteroatoms are detected using SMARTS patterns for nitro and carboxylate groups. For the remaining combinations of C–H

Gold extraction at the molecular level using α- and β-cyclodextrins

• Susana Santos Braga

Beilstein J. Org. Chem. 2025, 21, 1116–1125, doi:10.3762/bjoc.21.89

• structures of the complexes of α-CD with KAuCN2 salts, with tubular representation for α-CD and space-filling for KAu(CN)2. The images at the center depict the interaction between one Au(CN)2− anion and one or two α-CD(s), according to the stoichiometry. The images at the sides depict the packing for the 1:1

• stripping from activated carbon using α-cyclodextrin. J. Am. Chem. Soc. 2021, 143, 1984–1992. Copyright 2020 American Chemical Society. This content is not subject to CC BY 4.0. Solid-state structure of the complex 2β-CD·HAuBr4·DBC. (a) Capped-stick and space-filling representation of different views of 2β

• -CD·HAuBr4·DBC, showing the packing mode and dimensions of the complex. (b) Capped-stick and space-filling representation of the β-CD channels extending along the c-axis, which are occupied by alternating DBC and [AuBr4]− anions. Solvent molecules and hydrogen atoms have been omitted for the sake of clarity

Orthogonal photoswitching of heterobivalent azobenzene glycoclusters: the effect of glycoligand orientation in bacterial adhesion

• Leon M. Friedrich and
• Thisbe K. Lindhorst

Beilstein J. Org. Chem. 2025, 21, 736–748, doi:10.3762/bjoc.21.57

• in carbohydrate recognition on surfaces. Cartoon of the photoswitchable glycoconjugates investigated in this account. The previously described heterobivalent azobenzene glycocluster 6βGlc3αMan 1 [24] is shown as structural formula and as the corresponding symbolic representation; the other structural

• representation of the superimposed most stable ligand–protein complexes from IFD for E- and Z-6αMan 4 (A: E in red, Z in yellow) and E- and Z-3αMan 5 (B: E in turquoise, Z in magenta), as well as of the EE, ZZ, EZ, and ZE isomers of glycocluster 6βGlc3αMan 1 (C) and of 6αMan3αMan 2 (D). The protein FimH (1UWF

Photocatalyzed elaboration of antibody-based bioconjugates

• Marine Le Stum,
• Eugénie Romero and
• Gary A. Molander

Beilstein J. Org. Chem. 2025, 21, 616–629, doi:10.3762/bjoc.21.49

• , ultimately contributing to the broader landscape of targeted biomedicine. Representation of an antibody–drug conjugate. The antibody shown in this figure is from https://www.gettyimages.de/detail/illustration/monoclonal-antibody-igg2a-lizenfreie-illustration/585105259?searchscope=image%2Cfilm&adppopup=true

• BY 4.0. Representation of the photoinduced control of the DAR. The antibody shown in this figure is from https://www.gettyimages.de/detail/illustration/monoclonal-antibody-igg2a-lizenfreie-illustration/585105259?searchscope=image%2Cfilm&adppopup=true; ALFRED PASIEKA/SCIENCE PHOTO LIBRARY, No

• . 585105259. This content is not subject to CC BY 4.0. Representation of a photoinduced control of multi-payloads ADC strategy. The antibody shown in this figure is from https://www.gettyimages.de/detail/illustration/monoclonal-antibody-igg2a-lizenfreie-illustration/585105259?searchscope=image%2Cfilm&adppopup

Unprecedented visible light-initiated topochemical [2 + 2] cycloaddition in a functionalized bimane dye

• Metodej Dvoracek,
• Brendan Twamley,
• Mathias O. Senge and
• Mikhail A. Filatov

Beilstein J. Org. Chem. 2025, 21, 500–509, doi:10.3762/bjoc.21.37

• + 2] photocycloaddition of Cl2B. Parallelogram representation data of the reactive double bonds in a) all Cl2B structures, Me2B, and also syn-(H,Cl)bimane. Steady-state spectroscopic data from Me4B, Me2B, and Cl2B recorded in acetonitrile (ACN), dichloromethane (DCM), and toluene (TOL). Fluorescence

Hydrogen-bonded macrocycle-mediated dimerization for orthogonal supramolecular polymerization

• Wentao Yu,
• Zhiyao Yang,
• Chengkan Yu,
• Xiaowei Li and
• Lihua Yuan

Beilstein J. Org. Chem. 2025, 21, 179–188, doi:10.3762/bjoc.21.10

• supramolecular polymer in CHCl3/CH3CN (1:1, v/v, 298 K) at variable concentration. Variable-concentration 1H NMR spectra of the supramolecular polymer: (a) 2.0 mM, (b) 4.0 mM, (c) 6.0 mM, (d) 8.0 mM, and (e) 10 mM. a) Chemical structures of H-bonded macrocycles H1, H2, and guest G1, and schematic representation

• of the formation of a 2:2 host–guest complex. b) Chemical structure of guest G2. c) Schematic representation of the formation of a supramolecular polymer through orthogonal self-assembly upon addition of zinc ions. Supporting Information Supporting Information File 86: CheckCIF report for H2 ⊃ G1

Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning

• Pablo Quijano Velasco,
• Kedar Hippalgaonkar and
• Balamurugan Ramalingam

Beilstein J. Org. Chem. 2025, 21, 10–38, doi:10.3762/bjoc.21.3

• , automation in ASFRs reduces the need for constant human oversight, lowering operational costs and minimizing the risk of human errors. A schematic representation of ASFR is provided in Figure 5a. A self-optimizing microreactor system has been devised specifically for closed-loop optimization of the Heck

Surprising acidity for the methylene of 1,3-indenocorannulenes?

• Shi Liu,
• Märt Lõkov,
• Sofja Tshepelevitsh,
• Ivo Leito,
• Kim K. Baldridge and
• Jay S. Siegel

Beilstein J. Org. Chem. 2024, 20, 3144–3150, doi:10.3762/bjoc.20.260

• . The CL perspective provided us a different way to look at these compounds and interpret them as phenyl-substituted CpHs, thus reconciling their higher acidity. That perspective led us to a CL graph representation that predicted/rationalized additional acidic CpH-PAHs and sharpened our understanding of

• : [upper] defining the relationship of the molecular fragment to the graph node (shaded circle) for benzene, Cp anion, and indene anion; [middle], superposition of the graph node onto the molecular fragment and then representation of the nodes in the abstract graph: corannulene above phenanthrene (left

Synthesis of pyrrole-fused dibenzoxazepine/dibenzothiazepine/triazolobenzodiazepine derivatives via isocyanide-based multicomponent reactions

• Marzieh Norouzi,
• Mohammad Taghi Nazeri,
• Ahmad Shaabani and
• Behrouz Notash

Beilstein J. Org. Chem. 2024, 20, 2870–2882, doi:10.3762/bjoc.20.241

• washed with n-hexane and ethyl acetate solvent mixtures (1:3 for 3a and 3d; 2:1 for compounds 5). Representation of distinguished structures of benzodiazepine/benzoxazepine/benzothiazepine with pharmaceutical to electronic applications. The crystal structure of 4h (CCDC 2365305). The DNMR (dynamic

C–C Coupling in sterically demanding porphyrin environments

• Liam Cribbin,
• Brendan Twamley,
• Nicolae Buga,
• John E. O’ Brien,
• Raphael Bühler,
• Roland A. Fischer and
• Mathias O. Senge

Beilstein J. Org. Chem. 2024, 20, 2784–2798, doi:10.3762/bjoc.20.234

• (top right) with atomic displacements at 50% probability and hydrogen atoms omitted for clarity. Bottom left: Neoplastic representation of the molecular symmetry of compound 26. Bottom right: Neoplastic representation of the molecular symmetry of compound 27. Left: packing diagram of 27 viewed normal

• in the crystal. Displacement parameters shown at 50% probability and heteroatoms labelled only. Schematic representation of porphyrin 37 showing a doubly intercalated structure. Reaction scheme for the synthesis of OET-xBrPPs and subsequent Ni(II) metalation. Scope of arm-extended dodecasubstituted

Computational design for enantioselective CO₂ capture: asymmetric frustrated Lewis pairs in epoxide transformations

• Maxime Ferrer,
• Iñigo Iribarren,
• Tim Renningholtz,
• Ibon Alkorta and
• Cristina Trujillo

Beilstein J. Org. Chem. 2024, 20, 2668–2681, doi:10.3762/bjoc.20.224

• remarkable 96%ee yielded by the catalyst. Schematic representation of an (A) 2D and a (B) 3D volcano plot. The abbreviation “cat.” stands for catalyst. (A) Structure of PO annotated with the C–O bond distances and electron densities at the BCPs. BCPs are indicated by green spheres and the ring critical point

• by a red sphere. (B) Schematic representation of the two possible ring-opening reactions of PO in the presence of activated CO2. Symmetric FLP scaffolds considered in the first study. X denotes N or P. Subset of FLP scaffolds considered in the catalyst optimisation study. Substituents and labels are

Deciphering the mechanism of γ-cyclodextrin’s hydrophobic cavity hydration: an integrated experimental and theoretical study

• Stiliyana Pereva,
• Stefan Dobrev,
• Tsveta Sarafska,
• Valya Nikolova,
• Silvia Angelova,
• Tony Spassov and
• Todor Dudev

Beilstein J. Org. Chem. 2024, 20, 2635–2643, doi:10.3762/bjoc.20.221

• (CCW). Schematic representation of γ-CD–nH2O complexes (where n = 1–7) with water molecules/clusters located at different positions, and M062X/6-311++G(d,p)//M062X/6-31G(d,p) calculated relative enthalpies (ΔH78) of the respective complexes, in kcal mol−1. M062X/6-31G(d,p) optimized structures of the

Applications of microscopy and small angle scattering techniques for the characterisation of supramolecular gels

• Connor R. M. MacDonald and
• Emily R. Draper

Beilstein J. Org. Chem. 2024, 20, 2608–2634, doi:10.3762/bjoc.20.220

• change in the self-assembly, meaning a true representation of the supramolecular material may not be investigated which is rarely confirmed when applied to the characterisation of such systems. To identify such changes in structure, other characterisation techniques that probe the structures without the

Machine learning-guided strategies for reaction conditions design and optimization

• Lung-Yi Chen and
• Yi-Pei Li

Beilstein J. Org. Chem. 2024, 20, 2476–2492, doi:10.3762/bjoc.20.212

• efficiency and effectiveness of reaction conditions design, and enable novel discoveries in synthetic chemistry. Keywords: data preprocessing; reaction conditions prediction; reaction data mining; reaction optimization; reaction representation; Introduction Machine learning (ML) techniques have been widely

• schematic representation of the workflow in Figure 1. One approach to remove erroneous reactions is based on the concept of “catastrophic forgetting”, which refers to the model’s tendency to forget previously learned events during the training process. Toniato et al. [83] proposed to use this idea as a

• for reaction modeling The choice of featurization strategy for chemical reactions is crucial for building predictive models for reaction conditions. Compared to the extensive research on molecular representation learning, the development of reaction encoding methods is relatively less explored [93

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

• Stefan P. Schmid,
• Leon Schlosser,
• Frank Glorius and
• Kjell Jorner

Beilstein J. Org. Chem. 2024, 20, 2280–2304, doi:10.3762/bjoc.20.196

• transition metal catalysis [46][47][48] and biocatalysis [49][50][51], they are however not common for organocatalysis. Therefore, much research has been devoted to develop models that perform well on the available small data sets [52][53]. 1.2 Representation In order to be processed by any ML model, the

• data needs to be provided in a machine-readable way. Unlike chemists who typically use drawings of Lewis structures to represent molecules, computers require a numerical representation of the molecular structure. Since the information that describes the input directly influences what relationships a

• when describing coordination compounds as commonly found in transition metal catalysis for example [57]. Another kind of representation that has found considerable application for ML in organocatalysis, is the use of descriptors. These are sets of numerical or categorical values to encode a molecule. A

Finding the most potent compounds using active learning on molecular pairs

• Zachary Fralish and
• Daniel Reker

Beilstein J. Org. Chem. 2024, 20, 2152–2162, doi:10.3762/bjoc.20.185

• such, each molecule was processed individually, and predictions were made solely upon the representation of a single molecule. The datapoint with highest predicted potency was then added to the training set for the next iteration of active learning (Figure 1A). Conversely, during ActiveDelta learning

• algorithm (CHEMBL232-1, Alpha-1b adrenergic receptor). Admittedly, chemical selection trends across datasets are variable, and, as such, the following discussion is not universal but instead is a representation of the overall expected behavior of the algorithms. In the first learning iterations, AD-CP

Computational toolbox for the analysis of protein–glycan interactions

• Ferran Nieto-Fabregat,
• Maria Pia Lenza,
• Angela Marseglia,
• Cristina Di Carluccio,
• Antonio Molinaro,
• Alba Silipo and
• Roberta Marchetti

Beilstein J. Org. Chem. 2024, 20, 2084–2107, doi:10.3762/bjoc.20.180

• web services and software to build 2D and 3D models of carbohydrate structures is reported here. Notably, despite the existence of several encoding formats for glycans (Figure 3), significant efforts have been made in the years to enable a simple and standardised glycan representation, which would

• simplify the transmission and efficiency of the communication within the scientific community. This led to the extensive use of the symbol nomenclature for glycans (SNFG) representation that is used in all the tools described below (Figure 3) [11][45]. Numerous computer applications have been developed to

• a cartoon representation of glycans according to the symbol nomenclature for glycans (see Figure 3) (https://glycam.org/docs/othertoolsservice/downloads/downloads-software/index.html). Choosing the most appropriate simulation software package With the aim to accurately prepare glycans for MD

Methyltransferases from RiPP pathways: shaping the landscape of natural product chemistry

• Maria-Paula Schröder,
• Isabel P.-M. Pfeiffer and
• Silja Mordhorst

Beilstein J. Org. Chem. 2024, 20, 1652–1670, doi:10.3762/bjoc.20.147

• will be a useful tool for drug development; it will be intriguing to observe what the future holds. Schematic representation of the different acceptor regions for the methylation of RiPPs discussed in this article. Schematic overview of different methylation strategies for amino acids and peptides

• /aspartimide to isoaspartate. The methyl group is highlighted in blue. The reverse reaction with isoaspartate as substrate of PAMTs is also known. Structural organisation of the OphMA homodimer. A) Schematic representation. The MT domain is coloured in blue; the leader peptide is coloured in yellow, and the

pKalculator: A pK_a predictor for C–H bonds

• Rasmus M. Borup,
• Nicolai Ree and
• Jan H. Jensen

Beilstein J. Org. Chem. 2024, 20, 1614–1622, doi:10.3762/bjoc.20.144

• values for all deprotonated C–H sites using Equation 2: Machine learning The feature descriptor Recent research shows that the atomic descriptors introduced by Finkelmann et al. [30][31], using charge model 5 (CM5) atomic charges [32], are a great representation of atoms in molecules that can be used in

Novel route to enhance the thermo-optical performance of bicyclic diene photoswitches for solar thermal batteries

• Akanksha Ashok Sangolkar,
• Rama Krishna Kadiyam and
• Ravinder Pawar

Beilstein J. Org. Chem. 2024, 20, 1053–1068, doi:10.3762/bjoc.20.93

• . A schematic representation of the photoswitching mechanism operating in the studied BBD-based switches for MOST application is illustrated in Figure 1d. The parent BBD molecules absorb incoming photons from solar radiation and undergo photoinduced electronic excitation. The excited diene thereafter

• present work, (d) schematic representation of the photoswitching mechanism in the studied BBD-based photoswitches for molecular solar thermal energy storage applications. Energy storage capacities and barrier for back conversion of the photoproduct to the diene in the gas phase. (a) Storage energy, (b

Novel analogues of a nonnucleoside SARS-CoV-2 RdRp inhibitor as potential antivirotics

• Luca Julianna Tóth,
• Kateřina Krejčová,
• Milan Dejmek,
• Eva Žilecká,
• Blanka Klepetářová,
• Lenka Poštová Slavětínská,
• Evžen Bouřa and
• Radim Nencka

Beilstein J. Org. Chem. 2024, 20, 1029–1036, doi:10.3762/bjoc.20.91

• ) Graphical representation of the inhibitory activity of selected compounds evaluated from the gels obtained in the primer extension assay. The percentage of inhibition (against control) was plotted against the logarithm of the concentration of compounds. The results were fitted to sigmoidal dose–response

Monitoring carbohydrate 3D structure quality with the Privateer database

• Jordan S. Dialpuri,
• Haroldas Bagdonas,
• Lucy C. Schofield,
• Phuong Thao Pham,
• Lou Holland and
• Jon Agirre

Beilstein J. Org. Chem. 2024, 20, 931–939, doi:10.3762/bjoc.20.83

• of the tree is the glycan information, separated into glycan types. Each glycan also contains a list of sugars, with a range of validation information and a list of linkage with torsion angle information. Tree visualisation was created with jsoncrack.com. Left: Graphical representation of the

(Bio)isosteres of ortho- and meta-substituted benzenes

• H. Erik Diepers and
• Johannes C. L. Walker

Beilstein J. Org. Chem. 2024, 20, 859–890, doi:10.3762/bjoc.20.78