Iron-sulfur clusters are ubiquitous cofactors that mediate central biological processes. However, despite their long history, these metallocofactors remain challenging to investigate when coordinated to small (≤ six amino acids) oligopeptides in aqueous solution. In addition to being often unstable in vitro, iron-sulfur clusters can be found in a wide variety of forms with varied characteristics, which makes it difficult to easily discern what is in solution. This difficulty is compounded by the dynamics of iron-sulfur peptides, which frequently coordinate multiple types of clusters simultaneously. To aid investigations of such complex samples, a summary of data from multiple techniques used to characterize both iron-sulfur proteins and peptides is provided. Although not all spectroscopic techniques are equally insightful, it is possible to use several, readily available methods to gain insight into the complex composition of aqueous solutions of iron-sulfur peptides.

The development of chiral catalysts that can provide high enantioselectivities across a wide assortment of substrates or reaction range is a priority for many catalyst design efforts. While several approaches are available to aid in the identification of general catalyst systems, there is currently no simple procedure for directly measuring how general a given catalyst could be. Herein, we present a catalyst-agnostic workflow centered on unsupervised machine learning that enables the rapid assessment and quantification of catalyst generality. The workflow uses curated literature data sets and reaction descriptors to visualize and cluster chemical space coverage. This reaction network can then be applied to derive a catalyst generality metric through designer equations and interfaced with other regression techniques for general catalyst prediction. As validating case studies, we have successfully applied this method to identify-through-quantification the most general catalyst chemotype for an organocatalytic asymmetric Mannich reaction and predicted the most general chiral phosphoric acid catalyst for the addition of nucleophiles to imines. The mechanistic basis for catalyst generality can then be gleaned from the calculated values by deconstructing the contributions of chemical space and enantiomeric excess to the overall result. Finally, our generality techniques permitted the development of mechanistically informative catalyst screening sets that allow experimentalists to rationally select catalysts that have the highest probability of achieving a good result in the first round of reaction development. Overall, our findings represent a framework for interrogating catalyst generality, and this strategy should be relevant to other catalytic systems widely applied in asymmetric synthesis.

Practitioners are generally not willing to explore modern reactions where considerable synthetic effort is required to generate materials and the results are not certain. Organocatalysis exemplifies this, in which a broad set of enantioselective reactions have been successfully developed but further applications to include additional substrates are often not performed. Herein we demonstrate how statistical models can be utilized to accurately distinguish between different catalysts and reactions to guide the selection of efficient synthetic routes to obtain a target molecule.

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Selecting the optimal catalyst to impart high levels of enantioselectivity in a new transformation is challenging because the ideal molecular requirements of the catalyst for one reaction do not always simply translate to another. In these reaction scenarios practitioners typically use the most general catalyst structure as a starting point for optimization. However, for many reaction systems and catalyst chemotypes the most general catalyst structure may be largely unknown presenting a significant limitation in catalyst application to new reaction space. Herein, we demonstrate that comprehensive statistical models can be applied to identify the most general catalyst for many chemical systems. These inclusive statistical models that encompass many reaction types can provide information about the relevant structural requirements necessary for high enantioselectivity across a broad reaction range. By validating this approach on diverse regions of organocatalyzed reaction space we discovered structurally distinct catalysts can in some cases provide similar levels of enantioselectivity. The second curious finding determined that the best and most popular catalyst systems may not be equivalent. Validation of this approach on a multi-catalytic dearomatization reaction resulted in the discovery that our general catalyst findings allowed for streamlined reaction development for highly complex transformations.