The Reactivity of 4-Hydroxy- and 4-Silyloxy-1,5-allenynes with Homogeneous Gold(I) Catalysts

Site-specific template generative approach for retrosynthetic planning

Retrosynthesis, the strategy of devising laboratory pathways by working backwards from the target compound, is crucial yet challenging. Enhancing retrosynthetic efficiency requires overcoming the vast complexity of chemical space, the limited known interconversions between molecules, and the challenges posed by limited experimental datasets. This study introduces generative machine learning methods for retrosynthetic planning. The approach features three innovations: generating reaction templates instead of reactants or synthons to create novel chemical transformations, allowing user selection of specific bonds to change for human-influenced synthesis, and employing a conditional kernel-elastic autoencoder (CKAE) to measure the similarity between generated and known reactions for chemical viability insights. These features form a coherent retrosynthetic framework, validated experimentally by designing a 3-step synthetic pathway for a challenging small molecule, demonstrating a significant improvement over previous 5-9 step approaches. This work highlights the utility and robustness of generative machine learning in addressing complex challenges in chemical synthesis.

Ligand-controlled gold-catalyzed cycloisomerization of 1,n-enyne esters toward synthesis of dihydronaphthalene

We describe herein a gold-catalyzed rearrangement of propargyl esters followed by allene–ene cyclization to afford substituted bicyclic [4.4.0] dihydronaphthalene compounds.