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

Area of Science:

• Synthetic organic chemistry
• Computational chemistry
• Materials science

Background:

• Multicomponent reactions (MCRs) provide efficient synthesis of complex molecules.
• Traditionally, MCR discovery relied on serendipity or analogy.
• Computer-designed MCRs are emerging, but a hybrid approach is explored here.

Purpose of the Study:

• To report novel MCRs designed by analogy but yielding unexpected products.
• To utilize computational mechanistic-network search to elucidate reaction pathways.
• To highlight the synergy between human and computer-driven synthetic chemistry.

Main Methods:

• Initial MCR design based on analogy to known reaction classes.
• Experimental execution of designed MCRs with diverse substrates.
• Computational mechanistic-network search for product assignment and pathway elucidation.

Main Results:

• Discovery of novel MCRs with unexpected product distributions.
• Demonstration of high substrate-to-product complexity increase.
• Identification of photoactive scaffolds applicable as functional dyes.
• Successful assignment of most experimentally obtained products via computational analysis.

Conclusions:

• A hybrid approach combining analogy-based design and computational analysis is effective for MCR discovery.
• Novel MCRs offer access to complex, photoactive molecules with tunable properties.
• The methodology showcases a powerful synergy between human chemists and computational tools.