Mechanism to model: a physical organic chemistry approach to reaction prediction

• Published in: Chemical communications (Cambridge, England) England), 2023-09, Vol.59 (72), p.1711-1721
• Main Authors: Reid, Jolene P, Betinol, Isaiah O, Kuang, Yutao
• Format: Article
• Language: English
• Subjects: Asymmetry; Chemical reactions; Design of experiments; Enantiomers; Organic chemistry; Reaction mechanisms; Statistical models
• ISSN: 1359-7345; 1364-548X
• DOI: 10.1039/d3cc03229a

The application of mechanistic generalizations is at the core of chemical reaction development and application. These strategies are rooted in physical organic chemistry where mechanistic understandings can be derived from one reaction and applied to explain another. Over time these techniques have evolved from rationalizing observed outcomes to leading experimental design through reaction prediction. In parallel, significant progression in asymmetric organocatalysis has expanded the reach of chiral transfer to new reactions with increased efficiency. However, the complex and diverse catalyst structures applied in this arena have rendered the generalization of asymmetric catalytic processes to be exceptionally challenging. Recognizing this, a portion of our research has been focused on understanding the transferability of chemical observations between similar reactions and exploiting this phenomenon as a platform for prediction. Through these experiences, we have relied on a working knowledge of reaction mechanism to guide the development and application of our models which have been advanced from simple qualitative rules to large statistical models for quantitative predictions. In this feature article, we describe the models acquired to generalize organocatalytic reaction mechanisms and demonstrate their use as a powerful approach for accelerating enantioselective synthesis. Combining a working knowledge of reaction mechanism with statistical modelling is a powerful approach to prediction.