Computer-Generated, Mechanistic Networks Assist in Assigning the Outcomes of Complex Multicomponent Reactions

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dc.contributor.authorKrzeszewski, Maciej
dc.contributor.authorVakuliuk, Olena
dc.contributor.authorTasior, Mariusz
dc.contributor.authorWołos, Agnieszka
dc.contributor.authorRoszak, Rafał
dc.contributor.authorMolga, Karol
dc.contributor.authorTeimouri, Mohammad B.
dc.contributor.authorGrzybowski, Bartosz A.
dc.contributor.authorGryko, Daniel T.
dc.contributor.organizationInstitute of Organic Chemistry, Polish Academy of Sciences
dc.contributor.organizationAllchemy, Inc., United States
dc.contributor.organizationFaculty of Chemistry, Kharazmi University, Tehran, Iran
dc.contributor.organizationIBS Center for Algorithmic and Robotized Synthesis (CARS), Ulsan, South Korea
dc.contributor.organizationDepartment of Chemistry, UNIST, Ulsan, South Korea
dc.date.accessioned2025-05-09T11:46:36Z
dc.date.available2025-05-09T11:46:36Z
dc.date.issued2025
dc.description.abstractThe appeal of multicomponent reactions, MCRs, is that they can offer highly convergent, atom-economical access to diverse and complex molecules. Traditionally, such MCRs have been discovered “by serendipity” or “by analogy” but recently the first examples of MCRs designed by computers became known. The current work reports a situation between these extremes whereby the MCRs were initially designed by analogy to a known class but yielded unexpected results─at which point, mechanistic-network search performed by the computer was used to aid the assignment of the majority (though not all) of experimentally obtained products. The novel MCRs we report are of interest because they (i) have markedly different outcomes for substrates differing in relatively small structural detail; (ii) offer very high increase in substrate-to-product complexity; and (iii) enable access to photoactive scaffolds with potential applications as functional dyes. In a broader context, our results highlight a productive synergy between human and computer-driven analyses in synthetic chemistry.en
dc.description.sponsorshipPolish National Science Center, Poland (grant OPUS 2020/37/B/ST4/00017); Foundation for Polish Science (TEAM POIR.04.04.00-00-3CF4/16-00); European Union’s Horizon Europe Research and Innovation Programme under Grant Agreement No. 101161312; Polish National Science Centre (OPUS Grant NCN 2021/41/B/ST4/01617; Iran National Science Foundation, Project No. 4038780; Institute for Basic Science, Korea (project code IBS-R020-D1).
dc.identifier.citationJ. Am. Chem. Soc. 2025, 147, 15636−15644 // https://doi.org/10.1021/jacs.5c02846
dc.identifier.doi10.1021/jacs.5c02846
dc.identifier.issn0002-7863
dc.identifier.issn1943-2984
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/25824
dc.language.isoen
dc.publisherAmerican Chemical Society
dc.rightsUznanie autorstwa 4.0 Międzynarodoween
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceJournal of the American Chemical Society
dc.titleComputer-Generated, Mechanistic Networks Assist in Assigning the Outcomes of Complex Multicomponent Reactionsen
dc.typearticle
dc.type.versionpublishedVersion
person.identifier.orcidTeimouri, Mohammad B. [0000-0003-4178-9995]
person.identifier.orcidGrzybowski, Bartosz A. [0000-0001-6613-4261]
person.identifier.orcidGryko, Daniel T. [0000-0002-2146-1282]
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