Role of Electron-Deficient Olefin Ligands in a Ni-Catalyzed Aziridine Cross-Coupling To Generate Quaternary Carbons.

We previously reported the development of an electron-deficient olefin (EDO) ligand, Fro-DO, that promotes the generation of quaternary carbon centers via Ni-catalyzed Csp3-Csp3 cross-coupling with aziridines. By contrast, electronically and structurally similar EDO ligands such as dimethyl fumarate and electron-deficient styrenes afford primarily ß-hydride elimination side reactivity. Only a few catalyst systems have been identified that promote the formation of quaternary carbons via Ni-catalyzed Csp3-Csp3 cross-coupling. Although Fro-DO represents a promising ligand in this regard, the basis for its superior performance is not well understood. Here we describe a detailed mechanistic study of the aziridine cross-coupling reaction and the role of EDO ligands in facilitating Csp3-Csp3 bond formation. This analysis reveals that cross-coupling proceeds by a Ni0/II cycle with a NiII azametallacyclobutane catalyst resting state. Turnover-limiting C-C reductive elimination occurs from a spectroscopically observable NiII-dialkyl intermediate bound to the EDO. Computational analysis shows that Fro-DO accelerates turnover limiting reductive elimination via LUMO lowering. However, it is no more effective than dimethyl fumarate at reducing the barrier to Csp3-Csp3 reductive elimination. Instead, Fro-DO's unique reactivity arises from its ability to associate favorably to NiII intermediates. Natural bond order second-order perturbation theory analysis of the catalytically relevant NiII intermediate indicates that Fro-DO binds to NiII through an additional stabilizing donor-acceptor interaction between its sulfonyl group and NiII. Design of new ligands to evaluate this proposal supports this model and has led to the development of a new and tunable ligand framework.

Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning".

We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.

Predicting reaction performance in C-N cross-coupling using machine learning.

Machine learning methods are becoming integral to scientific inquiry in numerous disciplines. We demonstrated that machine learning can be used to predict the performance of a synthetic reaction in multidimensional chemical space using data obtained via high-throughput experimentation. We created scripts to compute and extract atomic, molecular, and vibrational descriptors for the components of a palladium-catalyzed Buchwald-Hartwig cross-coupling of aryl halides with 4-methylaniline in the presence of various potentially inhibitory additives. Using these descriptors as inputs and reaction yield as output, we showed that a random forest algorithm provides significantly improved predictive performance over linear regression analysis. The random forest model was also successfully applied to sparse training sets and out-of-sample prediction, suggesting its value in facilitating adoption of synthetic methodology.

Spectroscopic Study of the Interactions of Ruthenium-Ketoconazole Complexes of Known Antiparasitic Activity with Human Serum Albumin and Apotransferrin.

The pharmacological properties of any drug are related to their ability to interact with macromolecular blood components. The interaction of human serum albumin (HSA) and apotransferrin (ATf) with six RuII complexes containing ketoconazole (KTZ), which we have previously reported to be active against Leishmania major and Trypanosoma cruzi, has been investigated by monitoring the tryptophan fluorescence intensity of each protein upon incremental addition of the complexes. All the Ru-KTZ derivatives, namely cis-fac-[RuIICl2(DMSO)3(KTZ)] (1), cis-[RuIICl2(bipy)(DMSO)(KTZ)] (2), [RuII(η6-p-cymene)Cl2(KTZ)] (3), [RuII(η6-p-cymene)(en)(KTZ)][BF4]2 (4), [RuII(η6-p-cymene)(bipy)(KTZ)][BF4]2 (5), and [RuII(η6-p-cymene)(acac)(KTZ)][BF4] (6) are able to quench the intrinsic fluorescence of HSA and ATf at 27 °C. Analysis of the spectroscopic data using Stern-Volmer plots indicates that in both cases the quenching takes place principally through a static mechanism involving the formation of Ru complex-protein adducts; further analysis of the fluorescence data allowed the estimation of apparent association constants and the number of binding sites for each protein and each compound. The results indicate that both HSA and ATf are possible effective transporters for Ru-KTZ antiparasitic drugs.