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

• efficiency and effectiveness of reaction conditions design, and enable novel discoveries in synthetic chemistry. Keywords: data preprocessing; reaction conditions prediction; reaction data mining; reaction optimization; reaction representation; Introduction Machine learning (ML) techniques have been widely

• of SMILES without affecting their molecular structures or modifying specific functional groups in coupling reactions with chemistry-informed reaction templates. Despite the need for large amounts of data to train base models, the main advantage of text-based reaction representation is that it can be

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

• the development of ML innovation [117][118]. One example is the development of a new reaction representation based on the geometry of reactants and products [89]. Unlike expert-chosen descriptors, this representation is generalisable to other systems. Although not concerned with selectivity