Phenylene-bridged diphobane ligands with different substituents (CF3, H, OMe, (OMe)2, tBu) have been synthesized and applied as ligands in palladium-catalyzed carbonylation reactions of various alkenes. The performance of these ligands in terms of selectivity in hydroformylation versus alkoxycarbonylation has been studied using 1-hexene, 1-octene, and methyl pentenoates as substrates, and the results have been compared with the ethylene-bridged diphobane ligand (BCOPE). Hydroformylation of 1-octene in the protic solvent 2-ethyl hexanol results in a competition between hydroformylation and alkoxycarbonylation, whereby the phenylene-bridged ligands, in particular, the trifluoromethylphenylene-bridged diphobane L1 with an electron-withdrawing substituent, lead to ester products via alkoxycarbonylation, whereas BCOPE gives predominantly alcohol products (n-nonanol and isomers) via reductive hydroformylation. The preference of BCOPE for reductive hydroformylation is also seen in the hydroformylation of 1-hexene in diglyme as the solvent, producing heptanol as the major product, whereas phenylene-bridged ligands show much lower activities in this case. The phenylene-bridged ligands show excellent performance in the methoxycarbonylation of 1-octene to methyl nonanoate, significantly better than BCOPE, the opposite trend seen in hydroformylation activity with these ligands. Studies on the hydroformylation of functionalized alkenes such as 4-methyl pentenoate with phenylene-bridged ligands versus BCOPE showed that also in this case, BCOPE directs product selectivity toward alcohols, while phenylene-bridge diphobane L2 favors aldehyde formation. In addition to ligand effects, product selectivities are also determined by the nature and the amount of the acid cocatalyst used, which can affect substrate and aldehyde hydrogenation as well as double bond isomerization.

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Natural products are a rich resource of bioactive compounds for valuable applications across multiple fields such as food, agriculture, and medicine. For natural product discovery, high throughput in silico screening offers a cost-effective alternative to traditional resource-heavy assay-guided exploration of structurally novel chemical space. In this data descriptor, we report a characterized database of 67,064,204 natural product-like molecules generated using a recurrent neural network trained on known natural products, demonstrating a significant 165-fold expansion in library size over the approximately 400,000 known natural products. This study highlights the potential of using deep generative models to explore novel natural product chemical space for high throughput in silico discovery.

Natural products are a family of diverse compounds with multiple impactful applications, especially in therapeutics. Recent advances in genomics and bioinformatics have also hinted at vast untapped chemical potential within Nature. However, despite the many strategies available for activation and upregulation of natural product biosyntheses in native and heterologous microbial strains, there is yet to be a generalizable and e cient approach for interrogating diverse native strain collections. Here, we describe and demonstrate a exible and robust one-step integrase-mediated genetic-and cultivationbased approach to perturb and activate antibiotics production in a set of 54 actinobacterial strains. Our multi-pronged strategy signi cantly increases accessible metabolite space by two-fold, resulting in the discovery of the rst example of Gram-negative bioactivity in new tetramic acid analogs. We envision these results to serve as the rst step toward a more streamlined, accelerated, and scalable strategy to unlock the full potential of Nature's chemical repertoire.