Abstract:We report the development of a method to diastereoselectively access tetrasubstituted alkenes via nickel-catalyzed Suzuki-Miyaura cross-couplings of enol tosylates and boronic acid esters. Either diastereomeric product was selectively accessed from a mixture of enol tosylate starting material diastereomers in a convergent reaction by judicious choice of the ligand and reaction conditions. A similar protocol also enabled a divergent synthesis of each product isomer from diastereomerically pure enol tosylates. N… Show more
“…The kraken tool may enable informed catalyst design based on organophosphorus ligands, facilitate the optimization of reaction process parameters, inspire new ligand choices, and promote the synthesis of new organophosphorus compounds. The database and tools reported herein are currently being applied to enhance reaction optimization − and mechanistic workflows . The open-source nature of our codes, as well as the open database, is designed to be extended by others, and we welcome further contributions by the community.…”
The
design of molecular catalysts typically involves reconciling
multiple conflicting property requirements, largely relying on human
intuition and local structural searches. However, the vast number
of potential catalysts requires pruning of the candidate space by
efficient property prediction with quantitative structure–property
relationships. Data-driven workflows embedded in a library of potential
catalysts can be used to build predictive models for catalyst performance
and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III)
ligands providing comprehensive physicochemical descriptors based
on representative conformer ensembles. Using quantum-mechanical methods,
we calculated descriptors for 1558 ligands, including commercially
available examples, and trained machine learning models to predict
properties of over 300000 new ligands. We demonstrate the application
of kraken to systematically explore the property
space of organophosphorus ligands and how existing data sets in catalysis
can be used to accelerate ligand selection during reaction optimization.
“…The kraken tool may enable informed catalyst design based on organophosphorus ligands, facilitate the optimization of reaction process parameters, inspire new ligand choices, and promote the synthesis of new organophosphorus compounds. The database and tools reported herein are currently being applied to enhance reaction optimization − and mechanistic workflows . The open-source nature of our codes, as well as the open database, is designed to be extended by others, and we welcome further contributions by the community.…”
The
design of molecular catalysts typically involves reconciling
multiple conflicting property requirements, largely relying on human
intuition and local structural searches. However, the vast number
of potential catalysts requires pruning of the candidate space by
efficient property prediction with quantitative structure–property
relationships. Data-driven workflows embedded in a library of potential
catalysts can be used to build predictive models for catalyst performance
and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III)
ligands providing comprehensive physicochemical descriptors based
on representative conformer ensembles. Using quantum-mechanical methods,
we calculated descriptors for 1558 ligands, including commercially
available examples, and trained machine learning models to predict
properties of over 300000 new ligands. We demonstrate the application
of kraken to systematically explore the property
space of organophosphorus ligands and how existing data sets in catalysis
can be used to accelerate ligand selection during reaction optimization.
“…The specific workflow for the selection of a diverse molecule set adopted herein (Figure ) represents just an example of a more general procedure where different choices can be made at every step, depending on the application. Indeed, others have used similar procedures to generate diverse sets of molecules in unique contexts, ,,− including phosphorus ligand sets. ,− …”
In
reaction discovery, the search space of discrete reaction parameters
such as catalyst structure is often not explored systematically. We
have developed a tool set to aid the search of optimal catalysts in
the context of phosphine ligands. A virtual library, kraken, which is representative of the monodentate P(III)-ligand chemical
space, was utilized as the basis to represent the discrete ligands
as continuous variables. Using dimensionality reduction and clustering
techniques, we suggested a Phosphine Optimization Screening Set (PHOSS)
of 32 commercially available ligands that samples this chemical space
completely and evenly. We present the application of this screening
set in the identification of active catalysts for various cross-coupling
reactions and show how well-distributed sampling of the chemical space
facilitates identification of active catalysts. Furthermore, we demonstrate
how proximity in ligand space can be a useful guide to further explore
ligands when very few active catalysts are known.
“…Beyond trend evaluation, another powerful visualization technique for the identification of mechanism breaks are cutoff values in scatter plots. To streamline this approach, one can use computational ligand libraries that function as repositories of molecular parameters covering a broad chemical space. − Pioneering work by the Fey group established libraries mostly specializing in phosphine ligands, motivated by their prevalence in cross-coupling reactions. ,,− , More recently, a new library of monodentate phosphines, extended by ML techniques, was reported by the Aspuru-Guzik and Sigman groups . The library is incorporated in an online platform named Kraken, and 200 descriptors are available for each ligand in the database.…”
Section: Reaction Cliffsmentioning
confidence: 99%
“…Kraken has been used as the initial step in optimization campaigns, guiding the selection of ligands from different regions of the chemical space. These ligands have been evaluated using high-throughput experimentation (HTE) , and multiobjective optimization strategies . More recently, the Doyle and Sigman groups have demonstrated how the use of data available in Kraken can also drive new mechanistic discoveries .…”
The
chemical sciences are witnessing an influx of statistics into
the catalysis literature. These developments are propelled by modern
technological advancements that are leading to fast and reliable data
production, mining, and management. In organic chemistry, models encoded
with information-rich parameters have facilitated the formulation
of mechanistic hypotheses across different data-size regimes. Herein,
we aim to demonstrate through selected examples that the integration
of statistical principles into homogeneous catalysis can streamline
not only reaction optimization protocols but also mechanistic investigation
procedures. Namely, we highlight how different aspects of molecular
modeling, data set design, data visualization, and nuanced data restructuring
can contribute to improving chemical reactivity and selectivity, while
furthering our understanding of reaction mechanisms. By mapping out
these techniques at different data set sizes, we hope to encourage
the broad application of data-driven approaches for mechanistic studies
regardless of the accessible amount of data.
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