Predicting relative efficiency of amide bond formation using multivariate linear regression.

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Chemical Engineering

Background:

• Amide bond formation is crucial in synthesizing pharmaceuticals and natural products.
• Current methods for amide synthesis often lack predictable optimization for specific reactant pairs.
• Coupling reagents like carbonyldiimidazole (CDI) are commonly used but reaction conditions can be arbitrary.

Purpose of the Study:

• To develop predictive statistical models for amide formation reaction rates.
• To establish a data science workflow for selecting optimal training sets in chemical reactions.
• To understand the key molecular descriptors influencing carboxylic acid-amine coupling efficiency.

Main Methods:

• Utilized a data science workflow involving parameterization, dimensionality reduction, and clustering to select coupling partners.
• Measured reaction rates for a diverse set of carboxylic acid and primary alkyl amine couplings using CDI.
• Employed high-level density functional theory (DFT) descriptors for statistical model development.

Main Results:

• Collected reaction rates spanning five orders of magnitude, validating the broad chemical space coverage of the training set.
• Developed a statistical model demonstrating that carboxylic acid molecular features are the primary drivers of reaction rates.
• Validated the model's effectiveness and limitations through out-of-sample amide coupling reactions.

Conclusions:

• Predictive models for amide formation rates can be effectively developed using a data science approach.
• The choice of carboxylic acid significantly impacts the efficiency of CDI-mediated amide coupling.
• This methodology offers a pathway to optimize amide synthesis by predicting reaction outcomes.