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

• volumes of data and accelerate the prediction of optimal reaction condition combinations. It has been widely demonstrated that ML algorithms can be used for various chemistry-related tasks, such as yield prediction [14][15], site-selectivity prediction [16][17], reaction conditions recommendation [18

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

• . Keywords: catalyst design; machine learning; modelling; organocatalysis; selectivity prediction; Introduction Since the beginning of the 21st century, organocatalysts [1] have established themselves as a third group of homogeneous catalysts, next to biocatalysts [2] (enzymes) and transition metal-based

• , equipping experimentalists with the knowledge necessary to follow the developments in the field. The rest of the review is divided into three parts: (1) ML for reactivity and selectivity prediction, (2) ML for the design of privileged organocatalysts and (3) ML for catalyst and reaction design. Ultimately

• enantioselective formation of N,S-acetals catalysed by CPAs. To represent the catalysts, the authors developed the average steric occupancy (ASO) descriptors, a representation inspired by CoMFA [104][105][106], which recently also was applied in the selectivity prediction of aldehydes to nitroalkenes [68]. In ASO