Systematic, computational discovery of multicomponent and one-pot reactions

Rafał Roszak, Louis Gadina, Agnieszka Wołos, Ahmad Makkawi, Barbara Mikulak-Klucznik, Yasemin Bilgi, Karol Molga, Patrycja Gołębiowska, Oskar Popik, Tomasz Klucznik, Sara Szymkuć, Martyna Moskal, Sebastian Baś, Rafał Frydrych, Jacek Mlynarski, Olena Vakuliuk, Daniel T. Gryko () and Bartosz A. Grzybowski ()
Additional contact information
Rafał Roszak: Allchemy Inc.
Louis Gadina: Polish Academy of Sciences
Agnieszka Wołos: Allchemy Inc.
Ahmad Makkawi: Polish Academy of Sciences
Barbara Mikulak-Klucznik: Allchemy Inc.
Yasemin Bilgi: Polish Academy of Sciences
Karol Molga: Allchemy Inc.
Patrycja Gołębiowska: Polish Academy of Sciences
Oskar Popik: Polish Academy of Sciences
Tomasz Klucznik: Allchemy Inc.
Sara Szymkuć: Allchemy Inc.
Martyna Moskal: Allchemy Inc.
Sebastian Baś: Polish Academy of Sciences
Rafał Frydrych: Polish Academy of Sciences
Jacek Mlynarski: Polish Academy of Sciences
Olena Vakuliuk: Polish Academy of Sciences
Daniel T. Gryko: Polish Academy of Sciences
Bartosz A. Grzybowski: Polish Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-024-54611-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54611-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-54611-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().