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

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

• , previously unobserved by microscopy, was characterised. The fit of the full model identified the one-dimensional fibres formed by the stacking of molecules, while the low-q fit identified a larger clustering feature which could be described by the formation of asymmetric multifibre braided clusters. SANS

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

• unsupervised ML technique is clustering, which aims to group similar data points together and thus enables a diverse selection by uniformly sampling from the created space [67][68]. Supervised learning requires labelled data and aims at identifying correlations between the target values and the corresponding

• strongly related to each other, which complicates any quantitative interpretation of feature importance. Thus, thorough analysis and special strategies to address collinearity, such as hierarchical clustering [74] or threshold-based pre-selection [75] have to be considered to ensure reliable

• Field (AEIF), which are calculated for each CPA substituent (R) by observing the electrostatic potential of a quarternary ammonium ion with the substituent of interest (NMe3R+). The authors performed unsupervised clustering on an in silico library to select a ‘Universal Training Set’ (UTS) consisting of

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

• sulfoxide (DMSO, ε = 47.2) using GFN-FF-xTB [18] and the analytical linearized Poisson–Boltzmann (ALPB) equation [19] as the implicit solvation model. We then remove conformers with relative energies above 3 kcal/mol and select unique conformers by taking the centroids of a Butina clustering using pairwise

Mining raw plant transcriptomic data for new cyclopeptide alkaloids

• Draco Kriger,
• Michael A. Pasquale,
• Brigitte G. Ampolini and
• Jonathan R. Chekan

Beilstein J. Org. Chem. 2024, 20, 1548–1559, doi:10.3762/bjoc.20.138

• discovering diverse natural products from bacteria and fungi. In plants, this approach is often more limited due to the lack of available annotated genomes and transcriptomes combined with a less consistent clustering of biosynthetic genes. The recently identified burpitide class of ribosomally synthesized

• assembled transcriptomic data sets, it appeared that A. bettzickiana had the capacity to produce new cyclopeptide alkaloids. To evaluate this, a methanol extract of A. bettzickiana was prepared and analyzed by UHPLC-HRMS/MS. Subsequent GNPS networks did not show any clustering with cyclopeptide alkaloids

Activity assays of NnlA homologs suggest the natural product N-nitroglycine is degraded by diverse bacteria

• Kara A. Strickland,
• Brenda Martinez Rodriguez,
• Ashley A. Holland,
• Shelby Wagner,
• Michelle Luna-Alva,
• David E. Graham and
• Jonathan D. Caranto

Beilstein J. Org. Chem. 2024, 20, 830–840, doi:10.3762/bjoc.20.75

• structure of oligomeric proteins [31]. The highest ranked model is shown in Figure 5. AlphaFold predicted a canonical α/β fold with high confidence, between residues Arg16 and Gly146 (Figure 5A). Structural clustering using Foldseek Cluster identified substantial similarities to other PAS domain proteins

Genome mining of labdane-related diterpenoids: Discovery of the two-enzyme pathway leading to (−)-sandaracopimaradiene in the fungus Arthrinium sacchari

• Fumito Sato,
• Terutaka Sonohara,
• Shunta Fujiki,
• Akihiro Sugawara,
• Yohei Morishita,
• Taro Ozaki and
• Teigo Asai

Beilstein J. Org. Chem. 2024, 20, 714–720, doi:10.3762/bjoc.20.65

• are considered to be evolved by fusion and loss of domains. Following this model, CiCPS-PS might be ancestral and its duplication and subsequent loss of domains likely generate separate type TCs, such as AsCPS and AsPS (Figure 5B, model a). Alternatively, translocation and clustering of two

Phenanthridine–pyrene conjugates as fluorescent probes for DNA/RNA and an inactive mutant of dipeptidyl peptidase enzyme

• Josipa Matić,
• Tana Tandarić,
• Marijana Radić Stojković,
• Filip Šupljika,
• Zrinka Karačić,
• Ana Tomašić Paić,
• Lucija Horvat,
• Robert Vianello and
• Lidija-Marija Tumir

Beilstein J. Org. Chem. 2023, 19, 550–565, doi:10.3762/bjoc.19.40

• pKa = 6.3. We assumed that this value would not change much in the prepared conjugates, which confirms that both Phen-Py-1 and Phen-Py-2 are monoprotonated at pH 5. We submitted all four systems to molecular dynamics simulations and performed clustering analysis on the obtained structures. The most

• transitions are 265 and 357 nm (Phen-Py-1+), and 266 and 366 nm (Phen-Py-2+). They are discovered to be in very excellent agreement with the experimental findings shown in Table 1, which supports the computational strategy used here and demonstrates the reliability of the clustering analysis. This result is

• –pyrrole conjugates towards single stranded RNA systems [19][52]. To confirm that the described clustering analysis elucidated the most representative structures of each conjugate at both experimental pH values, we proceeded by calculating energies of the excited states responsible for the experimental UV

Computational model predicts protein binding sites of a luminescent ligand equipped with guanidiniocarbonyl-pyrrole groups

• Neda Rafieiolhosseini,
• Matthias Killa,
• Thorben Neumann,
• Niklas Tötsch,
• Jean-Noël Grad,
• Alexander Höing,
• Thies Dirksmeyer,
• Jochen Niemeyer,
• Christian Ottmann,
• Shirley K. Knauer,
• Michael Giese,
• Jens Voskuhl and
• Daniel Hoffmann

Beilstein J. Org. Chem. 2022, 18, 1322–1331, doi:10.3762/bjoc.18.137

• of the omega in the case without C-Raf; thirdly, above the pore between the two binding grooves of the omega in the presence of C-Raf. Overall, the clustering and the pattern of energies reflect the C2 symmetry of the 14-3-3ζ dimer. Minimum energy conformations Closer inspection of energies reveals

Identification of the new prenyltransferase Ubi-297 from marine bacteria and elucidation of its substrate specificity

• Jamshid Amiri Moghaddam,
• Huijuan Guo,
• Karsten Willing,
• Thomas Wichard and
• Christine Beemelmanns

Beilstein J. Org. Chem. 2022, 18, 722–731, doi:10.3762/bjoc.18.72

• (Figure S9 in Supporting Information File 1). Conclusion In silico analysis and homology clustering of Ptases encoded in Flavobacteria and the genus Sacchromonospora hinted towards a yet unexplored group of membrane-bound UbiA-like Ptase named UbiA-297, which is part of the conserved ebo gene sequences

• the PATRIC database (3.6.12) [34]. The sequence clustering was generated by the CLANS (CLuster ANalysis of Sequences) program [35]. In brief, the relationships between sequences were assessed based on an all-against-all BLAST search and the E-values better (lower) than 1.0E−6 were used to connect each

• biosynthesis of secondary metabolites (MGGBQ: 2-methyl-6-geranylgeranyl-1,4-benzoquinol and aurachin D). Homology clustering of Ptases encoded in marine Flavobacteria and Saccharomonospora species (G1–G4, colored nodes) and described Ptases in the Uniprot database (black 4-pointed stars). Visualization of the

Peptide stapling by late-stage Suzuki–Miyaura cross-coupling

• Hendrik Gruß,
• Rebecca C. Feiner,
• Ridhiwan Mseya,
• David C. Schröder,
• Michał Jewgiński,
• Kristian M. Müller,
• Rafał Latajka,
• Antoine Marion and
• Norbert Sewald

Beilstein J. Org. Chem. 2022, 18, 1–12, doi:10.3762/bjoc.18.1

• the macrocycles was analysed via principal component analysis (PCA) of the non-hydrogen atoms forming the cycle, and the structure of the peptide backbone was investigated via secondary structure analysis and backbone root mean square deviation (RMSD) clustering including amino acids Pro3 to Met15

• analysis on the macrocycle in the cis and trans isomer form of P5. PCA reveals that the first three principal components (PCs) respectively capture 39.0, 21.3, and 13.4% of the total variance in P5 cis, and 31.2, 25.4, and 9.9% in P5 trans. PCA-based clustering with a minimum distance of 4.0 Å in the three

• fairly high fraction of amino acids present in an anti-parallel β-sheet backbone conformation. aAxWt also shows a small fraction of anti-parallel β-sheet, while P5 trans only shows a marginal percentage of parallel β-structure. The backbone RMSD-based clustering further breaks down the conformational

A systems-based framework to computationally describe putative transcription factors and signaling pathways regulating glycan biosynthesis

• Theodore Groth,
• Rudiyanto Gunawan and
• Sriram Neelamegham

Beilstein J. Org. Chem. 2021, 17, 1712–1724, doi:10.3762/bjoc.17.119

• ). Regulatory modules were identified with graph clustering methods to identify groups of TFs that regulate common groups of glycogenes. Using our glycosylation pathway definitions, we used Fisher’s exact test to describe what kinds of glycosylation pathways were disproportionately over-represented in each

Volatile emission and biosynthesis in endophytic fungi colonizing black poplar leaves

• Christin Walther,
• Pamela Baumann,
• Katrin Luck,
• Beate Rothe,
• Peter H. W. Biedermann,
• Jonathan Gershenzon,
• Tobias G. Köllner and
• Sybille B. Unsicker

Beilstein J. Org. Chem. 2021, 17, 1698–1711, doi:10.3762/bjoc.17.118

• phylogenetic tree, we used the MUSCLE algorithm (gap open, −2.9; gap extend, 0; hydrophobicity multiplier, 1.2; max. iterations, 8; clustering method, upgmb) implemented in MEGA7 [89] to compute an amino acid alignment. Based on the MUSCLE alignment, the tree was constructed with MEGA7 using a Maximum

Comparative ligand structural analytics illustrated on variably glycosylated MUC1 antigen–antibody binding

• Christopher B. Barnett,
• Tharindu Senapathi and
• Kevin J. Naidoo

Beilstein J. Org. Chem. 2020, 16, 2540–2550, doi:10.3762/bjoc.16.206

• tools are applied to each of the simulation trajectories and this process was streamlined by using Galaxy workflows. The conformations of the antigens were analyzed using root-mean-square deviation, end-to-end distance, Ramachandran plots, and hydrogen bonding analysis. Additionally, RMSF and clustering

• equilibrium of the peptide backbone near the glycosylated residue for a 15-residue mucin peptide. The APDTRP fragment resembled an S-shaped bend and a clustering of low-energy conformations revealed structural similarities between glycosylated and nonglycosylated peptides [23]. The work by Movahedin et al

• antibody to understand the effect of glycosylation on antigen conformation during binding. With the rationale that a high-level overview can be used to understand the molecular changes, various analyses were considered: root-mean-square, end-to-end distance, clustering, φ–ψ backbone dihedral angles, and

Lipophilicity trends upon fluorination of isopropyl, cyclopropyl and 3-oxetanyl groups

• Benjamin Jeffries,
• Zhong Wang,
• Robert I. Troup,
• Anaïs Goupille,
• Jean-Yves Le Questel,
• Charlene Fallan,
• James S. Scott,
• Elisabetta Chiarparin,
• Jérôme Graton and
• Bruno Linclau

Beilstein J. Org. Chem. 2020, 16, 2141–2150, doi:10.3762/bjoc.16.182

• to have essentially the same lipophilicities. The correlation with the experimental values shows a coefficient of determination value of 0.776 (Figure 10A), which is worse than many of the investigated fragment-based clogP methods (see above), for which such clustering is not observed. When these

Clustering and curation of electropherograms: an efficient method for analyzing large cohorts of capillary electrophoresis glycomic profiles for bioprocessing operations

• Ian Walsh,
• Matthew S. F. Choo,
• Sim Lyn Chiin,
• Amelia Mak,
• Shi Jie Tay,
• Pauline M. Rudd,
• Yang Yuansheng,
• Andre Choo,
• Ho Ying Swan and
• Terry Nguyen-Khuong

Beilstein J. Org. Chem. 2020, 16, 2087–2099, doi:10.3762/bjoc.16.176

• qualitatively curate large cohort CE-LIF glycomics data. For glycan identification, a previously reported method based on internal triple standards is used. For determining the glycan relative quantities our method uses a clustering algorithm to ‘divide and conquer’ highly heterogeneous electropherograms into

• optimization study. The key advantage of this computational approach is that all runs can be analyzed simultaneously with high accuracy in glycan identification and quantitation and there is no theoretical limit to the scale of this method. Keywords: capillary electrophoresis; clustering; data analysis

• other tested fully automated software. Briefly, the method performs clustering analysis of glycomic electropherograms to group them into manageable clusters, followed by subsequent quantitation after semi-automated curation using the open source software HappyTools [16]. The clustering and migration

How and why plants and human N-glycans are different: Insight from molecular dynamics into the “glycoblocks” architecture of complex carbohydrates

• Carl A. Fogarty,
• Aoife M. Harbison,
• Amy R. Dugdale and
• Elisa Fadda

Beilstein J. Org. Chem. 2020, 16, 2046–2056, doi:10.3762/bjoc.16.171

• clustering analysis, see Supporting Information File 1 for all relevant Tables. Simulations were extended, if the sampling was not deemed sufficient, i.e., in case standard deviation values measured were significantly larger than 15° for each cluster in each trajectory. All trajectories were processed using

• cpptraj [31] and visually analysed with the Visual Molecular Dynamics (VMD) software package [34]. Root mean square deviation (RMSD) and torsion angles values were measured using VMD. A density-based clustering method was used to calculate the populations of occupied conformations for each torsion angle

• in a trajectory and heat maps for each dihedral were generated with a kernel density estimate (KDE) function. Statistical and clustering analysis was done with the R package and data were plotted with RStudio (https://www.rstudio.com). Further details on the simulation set-up and running protocol are

Automated high-content imaging for cellular uptake, from the Schmuck cation to the latest cyclic oligochalcogenides

• Rémi Martinent,
• Javier López-Andarias,
• Dimitri Moreau,
• Yangyang Cheng,
• Naomi Sakai and
• Stefan Matile

Beilstein J. Org. Chem. 2020, 16, 2007–2016, doi:10.3762/bjoc.16.167

• attractive candidates for intracellular delivery. Already a small dipeptide 9 with two Schmuck amino acids shows a strong binding (KD = 100 nM) and clustering ability towards heparin due to the strong noncovalent interaction between GCP and sulfate anions, such as glycosaminoglycans (GAGs) in heparin [26

• component analysis, diverse clustering methods, etc.), or for directly plotting specific parameters of interest. This fully automated workflow allows then to extract deep complex information from thousands of cells extremely rapidly and in a very consistent manner, allowing a very efficient comparison of

Opening up connectivity between documents, structures and bioactivity

• Christopher Southan

Beilstein J. Org. Chem. 2020, 16, 596–606, doi:10.3762/bjoc.16.54

• : clustering by content relatedness, position within citation networks, connections via authors or institutional affiliations. Assays: classified by various assay ontologies. Results: log transformations (e.g., pIC50 or pKi) for potency ranking and implicit molecular mechanism of action (mmoa), (e.g., where A

• -R indicates C to be a potent inhibitor of P). Compounds: a full range of cheminformatic analysis including 2D or 3D clustering, property prediction and chemical ontology assignments. Proteins: a full range of bioinformatic analysis including gene ontology (GO) assignments, pathway annotation

Nanangenines: drimane sesquiterpenoids as the dominant metabolite cohort of a novel Australian fungus, Aspergillus nanangensis

• Heather J. Lacey,
• Cameron L. M. Gilchrist,
• Andrew Crombie,
• John A. Kalaitzis,
• Daniel Vuong,
• Peter J. Rutledge,
• Peter Turner,
• John I. Pitt,
• Ernest Lacey,
• Yit-Heng Chooi and
• Andrew M. Piggott

Beilstein J. Org. Chem. 2019, 15, 2631–2643, doi:10.3762/bjoc.15.256

• Aspergillus genus, instead clustering within the sections Usti, Circumdati and Flavi (Figure S49 in Supporting Information File 1). The drimane sesquiterpenoids are not among the dominant metabolite classes commonly used for Aspergillus chemotaxonomy, but have been used for chemotyping of Aspergilli in the

Inherent atomic mobility changes in carbocation intermediates during the sesterterpene cyclization cascade

• Hajime Sato,
• Takaaki Mitsuhashi,
• Mami Yamazaki,
• Ikuro Abe and
• Masanobu Uchiyama

Beilstein J. Org. Chem. 2019, 15, 1890–1897, doi:10.3762/bjoc.15.184

• Hajime Sato Takaaki Mitsuhashi Mami Yamazaki Ikuro Abe Masanobu Uchiyama Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan Clustering of Pioneering Research (CPR) Advanced Elements Chemistry Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama

Identification of optimal fluorescent probes for G-quadruplex nucleic acids through systematic exploration of mono- and distyryl dye libraries

• Xiao Xie,
• Michela Zuffo,
• Marie-Paule Teulade-Fichou and
• Anton Granzhan

Beilstein J. Org. Chem. 2019, 15, 1872–1889, doi:10.3762/bjoc.15.183

• mirroring their conformations. Specifically, parallel G4s cluster in the upper left part of the plot (red dots), as a result of high fluorescence response with 18a and moderate-to-low response with 1p. Hybrid G4s (green dots) produce moderate light-up values for 18a and high ones for 1p, thus clustering on

Synthesis and conformational preferences of short analogues of antifreeze glycopeptides (AFGP)

• Małgorzata Urbańczyk,
• Michał Jewgiński,
• Joanna Krzciuk-Gula,
• Jerzy Góra,
• Rafał Latajka and
• Norbert Sewald

Beilstein J. Org. Chem. 2019, 15, 1581–1591, doi:10.3762/bjoc.15.162

• conformation [36][37]. In case of pentapeptide 4 the calculation resulted in 1 000 conformers. Cluster analysis based on all backbone dihedral angles allowed to identify multiclusters, containing approximately 1% of structures each. However, the clustering based only on the part of the main chain led to higher

• populated clusters. The biggest cluster has been found for clustering based on the φ, ψ torsion angles of Ala2-Thr3-Ala4. The biggest cluster contained more than 64% of all structures (see Table 4). Therefore, such observation can suggest high stability of the central part of the investigated glycopeptide

• , cleavage 10 × 5 min. Intramolecular hydrogen bonds – temperature coefficient -dδ/dT [ppb/K] in DMSO. Influence of used backbone dihedral onto cluster size of peptide 3. Characterization of conformational clusters obtained for clustering using backbone dihedrals for Ala1-Thr3 of peptide 3 and 4. Influence

Phylogenomic analyses and distribution of terpene synthases among Streptomyces

• Lara Martín-Sánchez,
• Kumar Saurabh Singh,
• Mariana Avalos,
• Gilles P. van Wezel,
• Jeroen S. Dickschat and
• Paolina Garbeva

Beilstein J. Org. Chem. 2019, 15, 1181–1193, doi:10.3762/bjoc.15.115

• synthases and three other minor clades. Not all the enzymes are clustering in the same way as their containing species based on the whole-genome phylogenetic analyses. For example, S. pactum ACT12 epi-isozizaene synthase clusters together with that of Streptomyces sp. 4F, while these two species were

New terpenoids from the fermentation broth of the edible mushroom Cyclocybe aegerita

• Frank Surup,
• Florian Hennicke,
• Nadine Sella,
• Maria Stroot,
• Steffen Bernecker,
• Sebastian Pfütze,
• Marc Stadler and
• Martin Rühl

Beilstein J. Org. Chem. 2019, 15, 1000–1007, doi:10.3762/bjoc.15.98

• of C. aegerita [16], two putative sesquiterpene synthase gene clusters have been identified on the basis of the published Δ6-protoilludene gene cluster of Omphalotus olearius [17]. The protein sequences of the genes clustering adjacent to the putative sesquiterpene synthase genes going by the gene