Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >550 bisphosphine ligands was constructed, and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs, suggesting a general strategy that can be readily implemented for reaction optimizations where performance is controlled by bisphosphine ligands.
Here we describe the use of crystalline ketones to control the fate of the radical pair intermediates generated in the Norrish type I photodecarbonylation reaction to render it a powerful tool in the challenging synthesis of sterically congested carbon-carbon bonds. This methodology makes the synthetically more accessible hexasubstituted ketones ideal synthons for the construction of adjacent, all-carbon substituted, stereogenic quaternary stereocenters. We describe here the structural and thermochemical parameters required of the starting ketone in order to react in the solid state. Finally, the scope and scalability of the reaction and its application in the total synthesis of two natural products is described.
Bis(cyclotryptamine) alkaloids have been popular topics of study for many decades. Five possible scaffolds for bis(cyclotryptamine) alkaloids were originally postulated in the 1950s, but only four of these scaffolds have been observed in natural products to date. We describe synthetic access to the elusive fifth scaffold, the piperidinoindoline, through syntheses of compounds now termed "dihydropsychotriadine" and "psychotriadine". The latter of these compounds was subsequently identified in extracts of the flower Psychotria colorata. Our synthetic route features a stereospecific solid-state photodecarbonylation reaction to introduce the key vicinal quaternary stereocenters.
Solid-state
photodecarbonylation is an attractive but underutilized
methodology to forge hindered C–C bonds in complex molecules.
This study discloses the use of this reaction to assemble the vicinal
quaternary stereocenter motif present in bis(cyclotryptamine) alkaloids.
Our strategy was enabled by experimental and computational investigations
of the role of substrate conformation on the success or failure of
the solid-state photodecarbonylation reaction. This informed a crystal
engineering strategy to optimize the key step of the total synthesis.
Ultimately, this endeavor culminated in the successful synthesis of
the bis(cyclotryptamine) alkaloid “psychotriadine,”
which features the elusive piperidinoindoline framework. Psychotriadine,
a previously unknown compound, was identified in the extracts of the
flower Psychotria colorata, suggesting
it is a naturally occurring metabolite.
Dynamic covalent chemistry-based sensors have recently emerged as powerful tools to rapidly determine the enantiomeric excess of organic small molecules. While a bevy of sensors have been developed, those for flexible molecules with stereocenters remote to the functional group that binds the chiroptical sensor remain scarce. In this study, we develop an iterative, datadriven workflow to design and analyze a chiroptical sensor capable of assessing challenging acyclic g-stereogenic alcohols. Following sensor optimization, the mechanism of sensing was probed with a combination of computational parameterization of the sensor molecules, statistical modeling, and high-level density functional theory (DFT) calculations. These were used to elucidate the mechanism of stereochemical recognition and revealed that competing attractive non-covalent interactions (NCIs) determine the overall performance of the sensor. It is anticipated that the data-driven workflows developed herein will be generally applicable to the development and understanding of dynamic covalent and supramolecular sensors.
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