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

• repository to be employed freely for research purposes. In addition to HappyTools, there are other available open-source Python packages to analyze chromatographic and spectroscopic data. A cross-platform Python package named Aston can be used to process both UV–vis and MS data. The open-source library is

• written using Python, NumPy, and SciPy and is openly hosted in an online repository [68]. Similarly, for processing chromatographic data from GC–FID, HPLC–UV, or HPLC–FD, packages are also available open source. Embedding these codes into HTE and ML workflow dramatically improves the efficiency and speed

• of the optimization processes. Liu et al. [69] developed a custom-built Python script to study the kinetics of carbonyldiimidazole-mediated amide formation by analyzing data from online HPLC and in-line FTIR-spectroscopic measurements. Their algorithm was able to automatically detect peaks from

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

• fluoride ion, respectively. Volcanic 1.3.3, a Python package for the NaviCat platform, was used to generate 3D volcano plots, facilitating the identification of the most appropriate catalyst for the coupling reaction being considered [27]. Volcano plots Volcano plots are a visualisation of the Sabatier

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

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

• python framework that allows users to prepare carbohydrate structures for atomistic simulations of complex glycoproteins, glycolipids and carbohydrate polymers in the GROMACS force field format (see below). Polysaccharides can be prepared by using the prepreader.py tool, glycoproteins and glycolipids by

• registered to the web site. 4. Azahar [53]: freely available python-based plugin that permits to visualise, analyse and model glycans and glycoconjugates (https://pymolwiki.org/index.php/Azahar). 5. 3D-SNFG: It is a script integrated in the visual molecular dynamics (VMD) program [54] (see below) that allows

A fiber-optic spectroscopic setup for isomerization quantum yield determination

• Anouk Volker,
• Jorn D. Steen and
• Stefano Crespi

Beilstein J. Org. Chem. 2024, 20, 1684–1692, doi:10.3762/bjoc.20.150

• . The background power was therefore monitored to get an accurate power reading before turning on the LED. The raw data was then fitted with a simple polynomial baseline using a Python script (see Supporting Information File 1). After baseline subtraction, the average power and standard deviation for

• different python scripts (provided in Supporting Information File 1). The rate equations are ordinary differential equations (ODEs) and can thus be solved numerically using existing methods in the SciPy package [50]. An absorbance function was constructed, which takes the solution of the ODEs to model the

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

• entry in the PDB [24] or in PDB-REDO [21]. For each structure in the PDB, the carbohydrate-containing chains are first identified before being validated using the suite of validation tools available within Privateer. Using the Python bindings available within the latest versions of Privateer, a

Optimizing reaction conditions for the light-driven hydrogen evolution in a loop photoreactor

• Pengcheng Li,
• Daniel Kowalczyk,
• Johannes Liessem,
• Mohamed M. Elnagar,
• Dariusz Mitoraj,
• Radim Beranek and
• Dirk Ziegenbalg

Beilstein J. Org. Chem. 2024, 20, 74–91, doi:10.3762/bjoc.20.9

• shot under red light, which matched the absorption properties of the dye, to obtain optimal optical visualization. Digital images extracted from videos (see Supporting Information File 2) were processed with the Python OpenCV package to quantify the mixing time (see Supporting Information File 3

• , Python code) by following the color evolution in the videos from red towards black under the red-light environment [49]. ROI used as the working zone were defined on the images when these were processed to quantify the mixing time. The three rectangles shown in Figure 3 on frame at 1.2 s showed the

• time quantification. For each frame of the video, the average red channel value of all the pixels in the marked ROIs were calculated. The mixing time calculation is explained in Supporting Information File 1 and the Python code for the corresponding calculation is shown in Supporting Information File 3

Cytochrome P450 monooxygenase-mediated tailoring of triterpenoids and steroids in plants

• Karan Malhotra and
• Jakob Franke

Beilstein J. Org. Chem. 2022, 18, 1289–1310, doi:10.3762/bjoc.18.135

• on triterpenoids and steroids. Amino acid sequences (149 triterpenoid CYPs, 266 non-triterpenoid CYPs) were aligned using MUSCLE [92], and a neighbour-joining consensus tree of 1,000 bootstrap replicates was generated using the Jukes–Cantor model. The final tree was visualised in Python using the

Heterogeneous metallaphotoredox catalysis in a continuous-flow packed-bed reactor

• Wei-Hsin Hsu,
• Susanne Reischauer,
• Peter H. Seeberger,
• Bartholomäus Pieber and
• Dario Cambié

Beilstein J. Org. Chem. 2022, 18, 1123–1130, doi:10.3762/bjoc.18.115

• time by automatically acquiring, processing and integrating the 19F NMR spectrum of the reaction mixture flowing in the spectrometer (see relevant code in Supporting Information File 3). In particular, the python packages flowchem [35] and nmrglue [36] were used to control the spectrometer and process

In-depth characterization of self-healing polymers based on π–π interactions

• Josefine Meurer,
• Julian Hniopek,
• Johannes Ahner,
• Michael Schmitt,
• Jürgen Popp,
• Stefan Zechel,
• Kalina Peneva and
• Martin D. Hager

Beilstein J. Org. Chem. 2021, 17, 2496–2504, doi:10.3762/bjoc.17.166

• along the scratch (every 20 µm) were performed with a 10 µm Rockwell indenter with the following parameters: 3 mN normal force, 200 µm/min scratch speed, length 1600 µm (P1) or 600 µm (P2). For the visualization and evaluation of the scratch profile data recorded by the MST3, a Python-based GUI

Simulating the enzymes of ganglioside biosynthesis with Glycologue

• Andrew G. McDonald and
• Gavin P. Davey

Beilstein J. Org. Chem. 2021, 17, 739–748, doi:10.3762/bjoc.17.64

• obesity and insulin resistance [36]. Glycologue web application The Glycologue ganglioside simulator is available at https://glycologue.org/g/, along with the source code of the simulator in the Python programming language. Glycologue exports networks as SBML, for import into Copasi [37], CellDesigner [38

Semiautomated glycoproteomics data analysis workflow for maximized glycopeptide identification and reliable quantification

• Steffen Lippold,
• Arnoud H. de Ru,
• Jan Nouta,
• Peter A. van Veelen,
• Magnus Palmblad,
• Manfred Wuhrer and
• Noortje de Haan

Beilstein J. Org. Chem. 2020, 16, 3038–3051, doi:10.3762/bjoc.16.253

• quantification in LaCyTools [16]. A python script was developed to facilitate this step (Supporting Information File 3). LaCyTools was chosen because it is open-source, can be applied for a large number of samples (thousands of samples in one study have been reported [19]), and allows data curation and

• data handling, making it an excellent tool for, e.g., clinical cohort analysis. In the current work, a python script was developed to streamline the connection between GlycopeptideGraphMS identification and LaCyTools quantification (Supporting Information File 3). All tools provided absolute values for

• quantification in LaCyTools (v 1.0.1) [16], the raw data were converted to the mzXML format by MSConvert. The generation of the LaCyTools analyte list was supported by an in-house Python (v 3.7.6) script (Supporting Information File 3), which converted a representative GlycopeptideGraphMS output to the required

Computational tools for drawing, building and displaying carbohydrates: a visual guide

• Kanhaya Lal,
• Rafael Bermeo and
• Serge Perez

Beilstein J. Org. Chem. 2020, 16, 2448–2468, doi:10.3762/bjoc.16.199

• for preparing carbohydrate structures for atomistic simulations of glycoproteins, carbohydrate polymers and glycolipids using GROMACS [70][71] In the form of Python scripts; the tools are used to prepare the system, which generally includes the processing of a.pdb file using the pdb2gmx tool

• computer code mostly written in C++ and Python languages. Rosetta is available to academic and commercial researchers through a license available at https://www.rosettacommons.org/software/license-and-download. The licence is free for academic users. The tool runs best on Linux or macOS platforms only. It

GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis

• Toan K. Phung,
• Cassandra L. Pegg and
• Benjamin L. Schulz

Beilstein J. Org. Chem. 2020, 16, 2127–2135, doi:10.3762/bjoc.16.180

• -glycosylation; O-glycosylation; Python; Introduction Glycosylation is a key post-translational modification critical for protein folding and function in eukaryotes [1][2][3]. Diverse types of glycosylation are known, all involving modification of specific amino acid residues with complex carbohydrate

• robustness and throughput. Our workflow uses a collection of scripts built on an in-house sequence string handling library and the scientific Python data handling package pandas [24], and integrates outputs of two commonly used software packages in glycoproteomic MS data analysis, Proteome Discoverer (Thermo

• Python 3.8.3 with backward compatibility tested up to Python 3.6. Each Byonic output file was first iteratively prepared for linking with AUC information from the Proteome Discoverer output. Using a regular expression pattern provided by UniProtKB, the UniProtKB accession ID of each protein from the

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

• the calibration algorithm see [16]. The calibrated electropherograms were then clustered. The clustering algorithm was implemented in-house using the SciPy python package. The clustering step consists of hierarchical clustering using a single linkage algorithm and forms flat clusters using the

In silico rationalisation of selectivity and reactivity in Pd-catalysed C–H activation reactions

• Liwei Cao,
• Mikhail Kabeshov,
• Steven V. Ley and
• Alexei A. Lapkin

Beilstein J. Org. Chem. 2020, 16, 1465–1475, doi:10.3762/bjoc.16.122

• modern supercomputer clusters [19]. The structures were generated by the Python module developed in house and explained in detail elsewhere [20]. Electronic energies were evaluated using Becke’s three-parameter hybrid B3LYP functional, while the molecular orbitals are expanded in triple-zeta all electron

• predictions. Employing the Python module [20] and OpenBabel executables [28], the 3D structures of the most stable conformers were generated from the 2D structure of a substrate. Subsequently, structures of all possible palladium intermediates representing both mechanisms (PA and SEAr) were built for each

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

• A. calidoustus SF006504 (GCA_001511075.1), A. ochraceus fc-1 (GCA_004849945.1), A. parasiticus SU-1 (GCA_000956085.1), A. sclerotiorum HBR18 (GCA_000530345.1) and A. ustus 3.3904 (GCA_000812125.1). Homologous gene clusters were identified in these genomes using a custom Python script, named

• was visualised using another custom Python script named crosslinker (https://github.com/gamcil/crosslinker). Further details regarding clusterblaster and crosslinker are given in Supporting Information File 1. Structures of nanangenines 1–10 isolated from A. nanangensis. Putative nanangenine

A permutation approach to the assignment of the configuration to diastereomeric tetrads by comparison of experimental and ab initio calculated differences in NMR data

• Przemysław J. Boratyński

Beilstein J. Org. Chem. 2017, 13, 2478–2485, doi:10.3762/bjoc.13.245

• diastereomers of compounds 1–7. Supporting Information Supporting Information File 460: Plots of NMR spectra for new compounds, HSQC experiments for tetrad 1, supporting tables, complete reference [16], synthetic references for compounds 1–4, presentation of manual workflow and Python code for automated

The digital code driven autonomous synthesis of ibuprofen automated in a 3D-printer-based robot

• Philip J. Kitson,
• Stefan Glatzel and
• Leroy Cronin

Beilstein J. Org. Chem. 2016, 12, 2776–2783, doi:10.3762/bjoc.12.276

• one-pot three-step approach. The synthesis of ibuprofen could be achieved on different scales simply by adjusting the parameters in the robot control software. The software for controlling the synthesis robot was written in the python programming language and hard-coded for the synthesis of ibuprofen

• open source allowing the printer to be easily interfaced with user-developed modifications, allowing us to produce our own software for coordinating the 3D printing, liquid handling and reaction timing (see Scheme 2). The software to control our robotic platform was written in Python and the full

Mechanical stability of bivalent transition metal complexes analyzed by single-molecule force spectroscopy

• Manuel Gensler,
• Christian Eidamshaus,
• Maurice Taszarek,
• Hans-Ulrich Reissig and
• Jürgen P. Rabe

Beilstein J. Org. Chem. 2015, 11, 817–827, doi:10.3762/bjoc.11.91

• constant velocities between 100 nm/s and 10 µm/s using a grid of different spots on the surface. Force–distance curves were processed as described in [27]. In short, signals were fitted according to the wormlike-chain model using Hooke, a Python-based force spectroscopy data analysis program [56]. Most

Integration of enabling methods for the automated flow preparation of piperazine-2-carboxamide

• Richard J. Ingham,
• Claudio Battilocchio,
• Joel M. Hawkins and
• Steven V. Ley

Beilstein J. Org. Chem. 2014, 10, 641–652, doi:10.3762/bjoc.10.56

• experience using the Python programming language [6] to control laboratory devices [5]. This language claims to be ideal for rapid development and the use of free software fosters collaboration [7], enabling technology to be transferred without the large initial set-up costs typically involved with

• and monitoring system being developed within our group [26], which can carry out a programmed sequence of operations written using the Python™ language. There is also a remote interface for observing the status of an ongoing reaction in real-time. In common with the industrial use of process

Camera-enabled techniques for organic synthesis

• Steven V. Ley,
• Richard J. Ingham,
• Matthew O’Brien and
• Duncan L. Browne

Beilstein J. Org. Chem. 2013, 9, 1051–1072, doi:10.3762/bjoc.9.118

• environment. However, the availability of powerful open-source image processing software such as the Python Imaging Library [68] (PIL, an image processing library for the Python [69] programming language) and OpenCV [70] (a C++ library for creating real-time computer vision applications with bindings for the

• Python language [71]) enables powerful image-recognition logic to be harnessed for a low cost and by nonspecialists. Access to these libraries is only one contributing factor: such software projects are often well documented with a number of examples, and the internet provides a medium for rapidly

• sharing and recycling code and applications. Another important aspect of software libraries such as these is that whilst they use highly efficient C or C++ code to perform processor-intensive calculations, the “bindings” allow their full functionality to be harnessed by using languages such as Python