RESUMO
Palladium-catalyzed cross-couplings of aryl chlorides usually call for bulky, electron-rich ligands such as phosphines or heterocyclic carbenes. We have now found that similarly powerful cross-coupling catalysts are obtained by the reaction of palladium salts with alkynyllithium reagents. The species initially formed in this process was characterized as a dilithium tetraalkinyl palladate complex. It catalyzes the coupling of aryl chlorides with the lithium salts of various terminal alkynes to give alkynyl arenes. The isolated Li-alkynyl-Pd complex also efficiently promotes the reaction of aryl, and allyl chlorides with (hetero)aryl-, alkyl-, and allyllithium compounds as well as lithium amides. None of these reactions proceeded in the presence of palladium salts alone. The preparative utility of this approach was demonstrated by the synthesis of 49â molecules, including pharmaceutically relevant compounds.
RESUMO
Peptides have been established as modular catalysts for various transformations. Still, the vast number of potential amino acid building blocks renders the identification of peptides with desired catalytic activity challenging. Here, we develop a machine-learning workflow for the optimization of peptide catalysts. First-in a hypothetical competition-we challenged our workflow to identify peptide catalysts for the conjugate addition reaction of aldehydes to nitroolefins and compared the performance of the predicted structures with those optimized in our laboratory. On the basis of the positive results, we established a universal training set (UTS) containing 161 catalysts to sample an in silico library of â¼30,000 tripeptide members. Finally, we challenged our machine learning strategy to identify a member of the library as a stereoselective catalyst for an annulation reaction that has not been catalyzed by a peptide thus far. We conclude with a comparison of data-driven versus expert-knowledge-guided peptide catalyst optimization.