Systematic, computational discovery of multicomponent and one-pot reactions

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Reaction Design

Background:

• Multicomponent reactions (MCRs) are valuable for synthesizing complex molecules efficiently in one step.
• Historically, MCRs have been discovered serendipitously, limiting their known diversity.
• Developing new MCRs is crucial for expanding accessible molecular scaffolds and synthetic economy.

Purpose of the Study:

• To demonstrate the autonomous design of mechanistically distinct MCRs using AI.
• To develop computational models for predicting MCR yields and identifying organocatalytic potential.
• To validate AI-designed MCRs through experimental synthesis.

Main Methods:

• Training computers on reaction mechanisms and physical-organic chemistry principles.
• Developing an algorithm for autonomous MCR design.
• Implementing kinetic rate approximation models for yield prediction.
• Experimental validation of designed reactions.

Main Results:

• Successfully designed a large number of mechanistically distinct MCRs autonomously.
• Accurately predicted reaction yields and identified promising candidates for organocatalysis.
• Experimental validation confirmed the algorithm's predictions across diverse reactions and products.

Conclusions:

• AI can autonomously design novel multicomponent reactions, overcoming historical discovery limitations.
• Computational prediction of reaction kinetics and catalytic potential is feasible.
• This approach significantly expands the toolkit for organic synthesis and reaction discovery.