ArticleJournal of the American Chemical Society2025
Computer-Generated, Mechanistic Networks Assist in Assigning the Outcomes of Complex Multicomponent Reactions.
Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The 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.
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Registered trials
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