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

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.