Adaptive experimentation and optimization in organic chemistry

• Artur M. Schweidtmann and
• Philippe Schwaller

Beilstein J. Org. Chem. 2025, 21, 2367–2368, doi:10.3762/bjoc.21.180

• have enabled this transformation. High-throughput experimentation platforms can now rapidly test large numbers of reaction conditions [3]. Machine learning algorithms can process complex chemical data to identify promising directions [4]. Closed-loop systems can autonomously design, execute, and

Pathway economy in cyclization of 1,n-enynes

• Hezhen Han,
• Wenjie Mao,
• Bin Lin,
• Maosheng Cheng,
• Lu Yang and
• Yongxiang Liu

Beilstein J. Org. Chem. 2025, 21, 2260–2282, doi:10.3762/bjoc.21.173

• , this strategy has demonstrated remarkable potential in pharmaceutical synthesis, where the rapid generation of molecular diversity from simple precursors is paramount. Looking forward, the fusion of pathway economy with machine learning algorithms and high-throughput experimentation holds promise for

Photocatalysis and photochemistry in organic synthesis

• Timothy Noël and
• Bartholomäus Pieber

Beilstein J. Org. Chem. 2025, 21, 1645–1647, doi:10.3762/bjoc.21.128

• thematic issue that visible light increases the reaction rate of palladium-catalyzed Negishi cross-couplings [34]. The integration of enabling technologies has also contributed to the success of photocatalytic organic synthesis [35]. Automated reaction platforms, high-throughput experimentation techniques

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

• light control might also be improved using photochemical transformations. Importantly, optimization of bioconjugation reactions with technologies such as high-throughput experimentation has already been applied on antibodies [51]. Bioorthogonality Bioorthogonal chemistry has transformed our capability

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

• technique for high-throughput experimentation [83]. A further area of development would be the possibility of integrating options for the in-situ monitoring of reactions, such as X-ray and Raman techniques [47][48][84][85][86][87]. In conclusion, in recognizing that both photochemistry and mechanochemistry

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

• reactors; data processing; high-throughput experimentation; machine learning; reaction optimization; Introduction Organic synthesis plays a crucial role in drug discovery, polymer synthesis, materials science, agrochemicals, and specialty chemicals. Their synthesis and process optimization require

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

• . The paper also identifies the current limitations and opportunities in this field, such as the data quality and availability, and the integration of high-throughput experimentation. The paper demonstrates how the combination of chemical engineering, data science, and ML algorithms can enhance the

• ]. However, the OFAT method is simplistic and may fail to identify the optimal reaction conditions, since it ignores the possible interactions among the experimental factors. With the rapid development of high-throughput experimentation (HTE) techniques and ML, it has become more feasible to collect large

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

• a major challenge in organic chemistry and restricts the applicability of literature data for statistical modelling [30]. Despite emerging high-throughput experimentation (HTE) pipelines [44][45], large datasets of high-quality are still scarce. While multiple large datasets are available for

• inspired a manifold of other groups to develop new ML techniques, including graph neural networks. With the continued rise of high-throughput experimentation in organocatalysis [40], we expect ML to be applied to more data sets in this domain to aid in answering a wider variety of research questions. For

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

• accelerated experimental technologies with evolutionary algorithms provides a potentially disruptive change in the way molecules and materials are designed. Recent reviews describe the application of evolutionary approaches to drug and materials discovery [5][6]. High-throughput experimentation The