Computational prediction of complex cationic rearrangement outcomes

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Chemical Synthesis

Background:

• Computer-assisted organic synthesis has seen renewed interest, with algorithms planning synthetic pathways.
• Current methods focus on substrate-to-product rules and are limited in analyzing reaction mechanisms.

Purpose of the Study:

• To develop a computational approach for analyzing complex organic reaction mechanisms, specifically cationic rearrangements.
• To create an algorithm that generates reaction networks, traces mechanistic steps, and predicts product distributions.

Main Methods:

• Utilizing a knowledge base of mechanistic steps, physical-organic chemistry rules, and quantum mechanical/kinetic calculations.
• Employing a reaction-network approach to analyze complex transformations.
• Deploying an algorithm available at https://HopCat.allchemy.net/ for rapid analysis.

Main Results:

• The algorithm generates mechanistic networks and predicts outcomes for complex cationic rearrangements within minutes.
• Validated through experiments predicting outcomes of tail-to-head terpene cyclizations under varying conditions (solution vs. capsule).
• Successfully analyzed complex reaction mixtures, demonstrating the algorithm's capability beyond known reaction types.

Conclusions:

• Computers can now rationalize and discover new, mechanistically complex organic transformations.
• This approach extends beyond manipulating known reactions to understanding intricate mechanistic pathways.
• The developed algorithm offers a powerful tool for predicting and analyzing complex chemical reactions.