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