Quantitative In Silico Prediction of the Rate of Protodeboronation by a Mechanistic Density Functional Theory-Aided...

Area of Science:

• Computational Chemistry
• Organic Chemistry

Background:

• Boronic acids are vital in industry, but protodeboronation is a challenging side reaction.
• Accurate computational reaction prediction is crucial for synthetic chemistry.

Purpose of the Study:

• To develop an algorithm for predicting the rate of protodeboronation in boronic acids.
• To provide chemists with a tool to mitigate side reactions in boronic acid chemistry.

Main Methods:

• Developed a computational algorithm based on a literature mechanistic model for protodeboronation.
• Utilized Density Functional Theory to determine energy differences for active mechanistic pathways.
• Validated the algorithm using leave-one-out cross-validation on 50 boronic acids.

Main Results:

• The algorithm accurately predicts protodeboronation rates.
• Validated predictions on 50 additional boronic acids, including industrially relevant compounds.
• Identified 7 distinct mechanistic pathways for protodeboronation.

Conclusions:

• The developed algorithm aids chemists in avoiding protodeboronation side reactions.
• This tool is valuable for reactions involving boronic acids, such as Suzuki-Miyaura and Chan-Evans-Lam couplings.
• Enhances the reliability and efficiency of synthetic planning in organic chemistry.