Systematic, computational discovery of multicomponent and one-pot reactions
Discovery of new types of reactions is essential to organic chemistry because it expands the scope of accessible molecular scaffolds and can enable more economical syntheses of existing structures. In this context, the so-called multicomponent reactions, MCRs, are of particular interest because they...
Gespeichert in:
Veröffentlicht in: | Nature communications 2024-11, Vol.15 (1), p.10285-13 |
---|---|
Hauptverfasser: | , , , , , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Discovery of new types of reactions is essential to organic chemistry because it expands the scope of accessible molecular scaffolds and can enable more economical syntheses of existing structures. In this context, the so-called multicomponent reactions, MCRs, are of particular interest because they can build complex scaffolds from multiple starting materials in just one step, without purification of intermediates. However, for over a century of active research, MCRs have been discovered rather than designed, and their number remains limited to only several hundred. This work demonstrates that computers taught the essential knowledge of reaction mechanisms and rules of physical-organic chemistry can design – completely autonomously and in large numbers – mechanistically distinct MCRs. Moreover, when supplemented by models to approximate kinetic rates, the algorithm can predict reaction yields and identify reactions that have potential for organocatalysis. These predictions are validated by experiments spanning different modes of reactivity and diverse product scaffolds.
Multi component reactions (MCRs) can build complex scaffolds from multiple starting materials in just one step without purification of intermediates but until now MCRs have been discovered rather than designed. Here, the authors demonstrate an algorithmic approach based in the knowledge of reaction mechanisms and rules of physical-organic chemistry to design autonomously MCRs in large numbers. |
---|---|
ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-024-54611-5 |