Photomechanochemistry: harnessing mechanical forces to enhance photochemical reactions

• Francesco Mele,
• Ana M. Constantin,
• Andrea Porcheddu,
• Raimondo Maggi,
• Giovanni Maestri,
• Nicola Della Ca’ and
• Luca Capaldo

Beilstein J. Org. Chem. 2025, 21, 458–472, doi:10.3762/bjoc.21.33

• milling conditions. Additionally, these methods are neither scalable nor typically suitable for automation. Therefore, it comes as no surprise that the examples described above bear little synthetic potential. The substantial technological advancements within the chemical field have impacted

Beyond symmetric self-assembly and effective molarity: unlocking functional enzyme mimics with robust organic cages

• Keith G. Andrews

Beilstein J. Org. Chem. 2025, 21, 421–443, doi:10.3762/bjoc.21.30

• , means there are opportunities in robust organic cage research not present in any other type of cavity currently explored. Automation and calculation in cavity synthesis and study The computational discovery of new materials has advanced in recent years with increased computational power [405]. Imine

• -based porous organic cages have been a popular choice for study [377][379][406][407][408], as have MOCs [208][376][386]. Much focus remains on the prediction (and automation) [409] of the formation of cavities by probing combinations of, e.g., amines/aldehydes or metals/ligands to identify structures

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

• chemical reaction optimization has been enabled by advances in lab automation and the introduction of machine learning algorithms. Therein, multiple reaction variables can be synchronously optimized to obtain the optimal reaction conditions, requiring a shorter experimentation time and minimal human

• demands solutions that meet multiple targets, such as yield, selectivity, purity, cost, environmental impact, etc. In recent years, the advancement of artificial intelligence (AI), machine learning (ML), and automation has produced a paradigm shift for chemical synthesis optimization techniques. By

• accelerate the discovery and development of organic molecules by the rapid screening and analysis of large numbers of experimental conditions simultaneously. For the purpose of this article, we define HTE as a technique that leverages a combination of automation, parallelization of experiments, advanced

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

• optimization platforms have been developed [188][189][190][191], which integrate software optimization algorithms with hardware automation for experiments, enabling large-scale experimentation and data collection. Among these, BO [192] is the most classic and widely used algorithm, which leverages kernel

• this interdisciplinary field. The integration of HTE with ML is revolutionizing how chemists approach reaction conditions. Future efforts should aim to enhance these technologies to enable faster and more comprehensive data collection, potentially leading to the automation of HTE and ML integration

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

• energies of relevant species either via force field or quantum chemical methods to assess the properties of a reaction such as activation energies or selectivity. Irrespective of the degree of automation, in silico calculations are often less time-sensitive than wet-lab experiments and can be used to

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

• optimization. Discussion Coinciding with increased enthusiasm for machine learning methods to support drug discovery [30][31], expanded use of adaptable laboratory automation [16][32][33] will help support adaptive learning methods like active machine learning to become a cornerstone technology to guide

Inline purification in continuous flow synthesis – opportunities and challenges

• Jorge García-Lacuna and
• Marcus Baumann

Beilstein J. Org. Chem. 2022, 18, 1720–1740, doi:10.3762/bjoc.18.182

• and mass transfer, improved safety and scalability as well as the potential of reaction telescoping and automation [1][2]. Consequently, researchers from academia and the fine chemical industry have established flow technology as a complementary and oftentimes superior approach to traditional batch

• exploit reaction automation [23][24][25] in combination with various inline analysis techniques to generate data-rich processes with immediate feedback loops [26][27][28][29][30]. Researchers must embrace all available options at the outset of a given project especially if a highly robust flow process

• high purity and productivity (22.7 gh−1L−1 vs 17.2 gh−1L−1) with reduced solvent consumption (0.3 L gh−1L−1 vs 1.4 gh−1L−1) when compared to batch purification. Automation of the process is also described to avoid offline operations. Örkényi and co-workers reported a centrifugal partition

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

• . Overall, the lack of catalyst separation and the possibility of combining the reactor with in-line analytical feedback enables the flow synthesis of C–S and C–O coupled products in a simple, versatile and amenable to automation way. Different approaches to heterogeneous photochemistry in flow. a) Serial

Automated grindstone chemistry: a simple and facile way for PEG-assisted stoichiometry-controlled halogenation of phenols and anilines using N-halosuccinimides

• Dharmendra Das,
• Akhil A. Bhosle,
• Amrita Chatterjee and
• Mainak Banerjee

Beilstein J. Org. Chem. 2022, 18, 999–1008, doi:10.3762/bjoc.18.100

• , reproducibility, and scalability. An alternative and efficient way of grinding is the use of automation instead of manual intervention. Notably, an industrial-scale synthesis is possible by the suitable choice of a large automated grindstone apparatus. However, there are only limited examples of the use of

A comprehensive review of flow chemistry techniques tailored to the flavours and fragrances industries

• Guido Gambacorta,
• James S. Sharley and
• Ian R. Baxendale

Beilstein J. Org. Chem. 2021, 17, 1181–1312, doi:10.3762/bjoc.17.90

• [14][28][29][38][39]. There are many reasons for adopting flow chemistry and continuous manufacturing practices in both industry and academia: Increased levels of automation, inherent health and safety implications due to containment, the implicit higher heat and mass transfer rates, higher

Valorisation of plastic waste via metal-catalysed depolymerisation

• Francesca Liguori,
• Carmen Moreno-Marrodán and
• Pierluigi Barbaro

Beilstein J. Org. Chem. 2021, 17, 589–621, doi:10.3762/bjoc.17.53

Molecular basis for protein–protein interactions

• Brandon Charles Seychell and
• Tobias Beck

Beilstein J. Org. Chem. 2021, 17, 1–10, doi:10.3762/bjoc.17.1

• analysis software and automation in liquid chromatography–mass spectrometry over the past decade made HDXMS an increasingly attractive tool for biochemists. In HDXMS, changes in the mass associated with the isotopic exchange between amide hydrogen atoms and surrounding deuterated solvent are measured. The

• painting. However, even though HDXMS is a very reproducible and straightforward method (albeit the experiment has to be carefully and properly performed), data analysis, even with recent software automation, is the major limitation as the interpretation requires a certain level of expertise. Biophysical

Tools for generating and analyzing glycan microarray data

• Akul Y. Mehta,
• Jamie Heimburg-Molinaro and
• Richard D. Cummings

Beilstein J. Org. Chem. 2020, 16, 2260–2271, doi:10.3762/bjoc.16.187

• : Available. Address: https://haablab.vai.org/tools/. Description: SignalFinder-Microarray is an image analysis tool which allows automation to begin one step before. The software is free to use for noncommercial uses. Using this tool a user can input simply the image obtained from the scanner (.tif file) and

• current automation tools are flawless, the need for new tools for the analysis and reporting of glycan microarray data is ever-present. Overview of a typical glycan microarray workflow, beginning with the obtention of glycans to analysis of binding data. Briefly, glycans are chemically or enzymatically

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

• post-quantitation. Thus, the approach is semi-automated, achieving the scale of automation while still maintaining the accuracy of manual assignment. We used this technique to comprehensively and accurately characterize the effect of multivariate bioprocessing conditions upon the glycosylation profile

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

• results can be obtained from dose–response curves of the targeted delivery to selected cells and the cytotoxicity in the same experiment, even with poorly optimized cellular systems. Keywords: automation; cell-penetrating peptides; cellular uptake; cytosolic delivery; cytotoxicity; high-content imaging

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

• the mechanistic models based on accurate quantum chemical methods, such as the density functional theory (DFT) methods, decreases. Automation of DFT, as well as using results of DFT to develop less expensive predictive models, are the two approaches that may offer the alternatives to the fully data

Photophysics and photochemistry of NIR absorbers derived from cyanines: key to new technologies based on chemistry 4.0

• Bernd Strehmel,
• Christian Schmitz,
• Ceren Kütahya,
• Yulian Pang,
• Anke Drewitz and
• Heinz Mustroph

Beilstein J. Org. Chem. 2020, 16, 415–444, doi:10.3762/bjoc.16.40

Understanding the unexpected effect of frequency on the kinetics of a covalent reaction under ball-milling conditions

• Ana M. Belenguer,
• Adam A. L. Michalchuk,
• Giulio I. Lampronti and
• Jeremy K. M. Sanders

Beilstein J. Org. Chem. 2019, 15, 1226–1235, doi:10.3762/bjoc.15.120

• parameterization and additional model features. Acknowledgements We thank: C. A. Bland for the mechanical and P. Donnelly for the software design of the automation of the grinders for repeat grinding; Richard Nightingale and his team from the mechanical workshop at the Department of Chemistry, University of

Assessing the possibilities of designing a unified multistep continuous flow synthesis platform

• Mrityunjay K. Sharma,
• Roopashri B. Acharya,
• Chinmay A. Shukla and
• Amol A. Kulkarni

Beilstein J. Org. Chem. 2018, 14, 1917–1936, doi:10.3762/bjoc.14.166

• ’, in reality, such an envisaged system would be much more complex than these examples. Keywords: automation; continuous flow synthesis; cybernetics; multistep flow synthesis; unified platforms; Review Introduction Flow chemistry is now seen as a reliable approach for the synthesis of simple organic

• . Automation: Reduced human intervention facilitated by in-line measurements, automated optimization programs and continuous operation for a controlled set of conditions will be the unique features that will make such platforms attractive and efficient. Reproducibility: Development of individual reaction steps

• /solvents/byproducts. Even the change in the sequence should be adaptable such a system can be very expensive as well. Skills: With the advent of many flow synthesis tools available in the market much of the above issues may be taken care of. However, the automation in multistep synthesis needs careful

Recyclable hypervalent-iodine-mediated solid-phase peptide synthesis and cyclic peptide synthesis

• Dan Liu,
• Ya-Li Guo,
• Jin Qu and
• Chi Zhang

Beilstein J. Org. Chem. 2018, 14, 1112–1119, doi:10.3762/bjoc.14.97

• ]. Compared with classical solution-phase peptide synthesis, the fast development of SPPS is mainly due to its short reaction time, high efficiency, low racemization, simple work-up and automation. In recent decades, various strategies, for example, native chemical ligation (NCL) [32] and serine/threonine

A concise flow synthesis of indole-3-carboxylic ester and its derivatisation to an auxin mimic

• Marcus Baumann,
• Ian R. Baxendale and
• Fabien Deplante

Beilstein J. Org. Chem. 2017, 13, 2549–2560, doi:10.3762/bjoc.13.251

• and trace amounts of ethyl cyanoacetate (10). Finally, manual separation followed by drying over Na2SO4 and solvent evaporation gave the desired product 11 in 94–96% yield, based upon 6 sampled aliquots of 20 min each processing time. Several laboratory approaches to the automation of batch separation

Preactivation-based chemoselective glycosylations: A powerful strategy for oligosaccharide assembly

• Weizhun Yang,
• Bo Yang,
• Sherif Ramadan and
• Xuefei Huang

Beilstein J. Org. Chem. 2017, 13, 2094–2114, doi:10.3762/bjoc.13.207

• are intrinsically sensitive to factors including protective groups on the glycan ring, reaction solvent, and additives present. As a result, further experimentation and analysis are needed to enable robust syntheses and achieve automation with comparable efficiencies of automated peptide and nucleic

Peptide synthesis: ball-milling, in solution, or on solid support, what is the best strategy?

• Ophélie Maurin,
• Pascal Verdié,
• Gilles Subra,
• Frédéric Lamaty,
• Jean Martinez and
• Thomas-Xavier Métro

Beilstein J. Org. Chem. 2017, 13, 2087–2093, doi:10.3762/bjoc.13.206

• . Therefore, for the deprotection no comparison of the reaction times between the two strategies (BM and solution) was possible. Similarly, the speed of reaction was not measured for SPPS, as automation of the coupling and deprotection steps enabled to save a considerable amount of time compared to ball

• regulations [39], one can easily foresee that the extremely low environmental impact of BM will be a determining advantage in the future. Time and money saved by automation of coupling and deprotection steps in SPPS could be transformed into a crippling burden when considering costs and environmental impact

• deprotection steps, demonstration of the feasibility to synthesize longer peptides, as well as automation of the coupling and deprotection steps would undoubtedly bring peptide synthesis by ball-milling to be the method of choice for peptide synthesis in laboratories, as well as for industrial production

A recursive microfluidic platform to explore the emergence of chemical evolution

• David Doran,
• Marc Rodriguez-Garcia,
• Rebecca Turk-MacLeod,
• Geoffrey J. T. Cooper and
• Leroy Cronin

Beilstein J. Org. Chem. 2017, 13, 1702–1709, doi:10.3762/bjoc.13.164

• increase in size compared to their neighbours. This demonstrates the ability of our platform to explore some different physicochemical conditions, combining the efficiency and unbiased nature of automation with our ability to select droplets as functional units based on simple criteria. Keywords

• monitored in real-time, but this is not a requirement for the droplet sorting and selection to proceed. Also, unlike active sorting, passive sorting is not reliant upon automation, and is therefore technically less complex. In both systems, droplets below a critical size threshold for fitness are discarded

Biomimetic molecular design tools that learn, evolve, and adapt

• David A Winkler

Beilstein J. Org. Chem. 2017, 13, 1288–1302, doi:10.3762/bjoc.13.125

• , medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis

• already obvious that a synergistic combination of robotics and automation with machine learning and evolutionary algorithms will lead to a step change in the ability to discover, design, and optimize molecules and more complex materials with useful properties thought to be inaccessible in the past. If