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

• found widespread application in organocatalysis, supervised and unsupervised learning are widely employed techniques. The latter uses unlabelled data (e.g., data without a label or numerical value), to identify patterns and relationships within the provided data. Popular tools are Principal Component

• Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP) [65], or t-distributed Stochastic Neighbour Embedding (t-SNE) [66], which have found application in organocatalysis to reduce the dimension of the respective reaction space, e.g., for visualization purposes. Another widely applied

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

• radial chemical fingerprints (Morgan Fingerprint, radius 2, 2048 bits, rdkit.org). Principal component analysis (PCA) was first performed to reduce the 2048 input dimensions to 50 dimensions before t-distributed Stochastic Neighbor Embedding (t-SNE) was applied to further reduce these 50 dimensions to 2

Discrimination of β-cyclodextrin/hazelnut (Corylus avellana L.) oil/flavonoid glycoside and flavonolignan ternary complexes by Fourier-transform infrared spectroscopy coupled with principal component analysis

• Nicoleta G. Hădărugă,
• Gabriela Popescu,
• Dina Gligor (Pane),
• Cristina L. Mitroi,
• Sorin M. Stanciu and
• Daniel Ioan Hădărugă

Beilstein J. Org. Chem. 2023, 19, 380–398, doi:10.3762/bjoc.19.30

• principal component analysis (FTIR–PCA). These innovative complexes combine the characteristics of the three components and improve the properties of the resulting material such as the onsite protection against oxidative degradation of hazelnut oil unsaturated fatty acid glycerides. Also, the apparent water

• suitable for the evaluation of such ternary complexes. The coupling of FTIR or other spectroscopic or chromatographic techniques with a multivariate statistical analysis method (e.g., principal component analysis, PCA) allows the evaluation of the similarity/dissimilarity of complexes, as well as the

• groups and stretching vibrations of the C–C groups, respectively (see Figure 2 and Figure 3 and Supporting Information File 1, Figures S6–S11 and Tables S4, and S6–S9). Discrimination of ternary complexes by Fourier-transform infrared spectroscopy coupled with principal component analysis (FTIR–PCA

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

• water. In vitro binding affinities to β-catenin determined by competitive fluorescence polarisation assays. Cleavage sites of Proteinase K digestion indicated by a red arrow. Principal component analysis (PCA) of the macrocycle’s non-hydrogen atoms in the two isomers of P5. The upper panel depicts the

A new glance at the chemosphere of macroalgal–bacterial interactions: In situ profiling of metabolites in symbiosis by mass spectrometry

• Marine Vallet,
• Filip Kaftan,
• Veit Grabe,
• Fatemeh Ghaderiardakani,
• Simona Fenizia,
• Aleš Svatoš,
• Georg Pohnert and
• Thomas Wichard

Beilstein J. Org. Chem. 2021, 17, 1313–1322, doi:10.3762/bjoc.17.91

• ) (Figure 1b and Table S1 in Supporting Information File 1). The principal component analysis (PCA) visualised differences between metabolic profiles of axenic algae, algae in symbiosis, and specialised tissues (thallus, rhizoidal zone), analysed either with LDI or MALDI-HRMS. The metabolic profiles of

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

• 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

• associated to the master object (the cell), a set of meaningful data can be extracted from each object in the different fluorescent channels. The data set generated for each cell can later be used for a deep phenotypic analysis, using an advanced unsupervised statistical analysis method (e.g., principal

Combination of multicomponent KA² and Pauson–Khand reactions: short synthesis of spirocyclic pyrrolocyclopentenones

• Riccardo Innocenti,
• Elena Lenci,
• Gloria Menchi and
• Andrea Trabocchi

Beilstein J. Org. Chem. 2020, 16, 200–211, doi:10.3762/bjoc.16.23

• were analyzed in terms of chemical properties and shape analysis in the context of the chemical space [61] using principal component analysis (PCA) and principal moments of inertia (PMI) analysis. PCA is a statistical tool to condense multidimensional chemical properties (i.e., molecular weight, logP

Bacterial terpene biosynthesis: challenges and opportunities for pathway engineering

• Eric J. N. Helfrich,
• Geng-Min Lin,
• Christopher A. Voigt and
• Jon Clardy

Beilstein J. Org. Chem. 2019, 15, 2889–2906, doi:10.3762/bjoc.15.283

• component analysis [140], qPCR/proteomics [141], and iterative grid search [142] to troubleshoot and guide the tuning of the promoter strengths and copy numbers of each module/enzyme (Figure 11b). Toxicity of biosynthetic intermediates, endogenous regulation, and stability of genetic constructs are the main

• engineer more active pathway enzyme variants [136][137][138]. As the balanced pathway expression is often the key to high terpenoid titers, these pathway genes have been grouped into “modules”, using a multivariate-modular approach [80], in vitro enzyme activity assays [139], proteomics-based principal

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

• the data matrix presented in Figure 5 using principal component analysis (PCA). TBA and c-myb, which had proven mostly unresponsive, were excluded from the analysis. The response pattern of each dye is represented as a dot in the plot of the two first principal components (PC1 vs PC2, Figure 7). In

• RNA) is plotted against the corresponding average light-up factor obtained with 12 G4 targets. The grouping of dyes is arbitrary and serves to highlight some of the structural motifs. Of note, x and y axes display different scales. PC1 vs PC2 plot obtained from the principal component analysis of the

Unexpected polymorphism during a catalyzed mechanochemical Knoevenagel condensation

• Sebastian Haferkamp,
• Andrea Paul,
• Adam A. L. Michalchuk and
• Franziska Emmerling

Beilstein J. Org. Chem. 2019, 15, 1141–1148, doi:10.3762/bjoc.15.110

• , using multivariate methods to analyze the reaction. Principal component analysis (PCA) is a bilinear modelling method [25][26] that helps to extract the main information from multi-dimensional data (here a time series of Raman spectra). The information contained within the original spectral variables is

• diffraction data. Results of multivariate data analysis of Raman spectra for 30 Hz milling experiments. Principal component analysis (PCA) of Raman data, showing (a) scores, and (b) loadings for (black) PC1 and (blue) PC2. (a) PC1 scores show a decrease in spectral component of 1a in the first minutes – cf

Strong hyperconjugative interactions limit solvent and substituent influence on conformational equilibrium: the case of cis-2-halocyclohexylamines

• Camila B. Francisco,
• Cleverton S. Fernandes,
• Ulisses Z. de Melo,
• Roberto Rittner,
• Gisele F. Gauze and
• Ernani A. Basso

Beilstein J. Org. Chem. 2019, 15, 818–829, doi:10.3762/bjoc.15.79

• calculations (M06-2X/6-311++G(2df,2p)) were in agreement with the experimental, with no influence of the solvent or the halogen on the equilibrium. A principal component analysis of natural bond orbital energies pointed to the σ*C–X and σC–H orbitals and the halogen lone pairs (LPX) as the most significant for

• . This arouses the interest for a deeper analysis of hyperconjugative interactions. Principal component analysis (PCA) of natural bond orbitals (NBOs) In order to interpretate the large number of interactions present in the compound structures, we applied PCA to NBO energies. For each individual bond

• preference for the ea conformer in cis-2-halocyclohexylamines, being strong enough to restrain the shift in the equilibrium due to other factors such as steric repulsion or solvent effects. Keywords: conformational equilibrium; cyclohexane derivatives; dynamic NMR; hyperconjugation; principal component

Computational methods in drug discovery

• Sumudu P. Leelananda and
• Steffen Lindert

Beilstein J. Org. Chem. 2016, 12, 2694–2718, doi:10.3762/bjoc.12.267

Is conformation a fundamental descriptor in QSAR? A case for halogenated anesthetics

• Maria C. Guimarães,
• Mariene H. Duarte,
• Josué M. Silla and
• Matheus P. Freitas

Beilstein J. Org. Chem. 2016, 12, 760–768, doi:10.3762/bjoc.12.76

• X1876 and X5979 in the structural scaffold. Therefore, principal component analysis (PCA) was applied to obtain information on how these coordinates affect the bioactivity in terms of the two principal components PC1 (56.59%) and PC2 (43.41%) (Figure 4). Since PCA is a pattern recognition tool, the set

Art, auto-mechanics, and supramolecular chemistry. A merging of hobbies and career

• Eric V. Anslyn

Beilstein J. Org. Chem. 2016, 12, 362–376, doi:10.3762/bjoc.12.40

• from all the receptors would need to be interpreted by a chemometric protocol [90], such as principal component analysis (PCA), linear discriminate analysis (LDA), hierarchical cluster theory (HCT), or an ANN. A course at Georgia Tech University, held in approximately the year 2000, was my basic

Copper-catalysed asymmetric allylic alkylation of alkylzirconocenes to racemic 3,6-dihydro-2H-pyrans

• Emeline Rideau and
• Stephen P. Fletcher

Beilstein J. Org. Chem. 2015, 11, 2435–2443, doi:10.3762/bjoc.11.264

• asymmetric induction (particularly in transformations where mechanisms are not understood) is extremely challenging, and it is not obvious how to parameterize the multiple variables present in key factors such as ligand structure [38]. Nevertheless, a Principal Component Analysis using JMP® 12.1.0 (SAS) in 3

Thermal and oxidative stability of the Ocimum basilicum L. essential oil/β-cyclodextrin supramolecular system

• Daniel I. Hădărugă,
• Nicoleta G. Hădărugă,
• Corina I. Costescu,
• Ioan David and
• Alexandra T. Gruia

Beilstein J. Org. Chem. 2014, 10, 2809–2820, doi:10.3762/bjoc.10.298

• coefficient), which were used in the statistical multivariate analysis, were calculated according to http://www.molinspiration.com/cgi-bin/properties, Molinspiration Cheminformatics. A principal component analysis (PCA) program was developed in-house in a bi-dimensional space regarding the above mentioned

• correspond to those from Table 1 and Table 2. Supporting Information Supporting Information File 642: Complexation data, principal component analysis data, GC–MS analysis chromatograms and mass spectra for O. basilicum L. essential oil (raw, degraded or recovered from the β-CD complexes).