Methods to access chiral sulfur (VI) pharmacophores are of interest in medicinal and synthetic ch... more Methods to access chiral sulfur (VI) pharmacophores are of interest in medicinal and synthetic chemistry. We report the desymmetrization of unprotected sulfonimidamides via asymmetric acylation with a cinchona-phosphinate catalyst. The desired products are formed in excellent yield and enantioselectivity with no observed bis-acylation. A data science-driven approach to substrate scope evaluation was coupled to high throughput experimentation (HTE) to facilitate statistical modeling in order to inform mechanistic studies. Reaction kinetics, catalyst structural studies, and density functional theory (DFT) transition state analysis elucidated the turnover-limiting step to be the collapse of the tetrahedral intermediate and provided key insights into the catalyst-substrate structure-activity relationships responsible for the origin of enantioselectivity. This study offers a reliable method for accessing enantioenriched sulfonimidamides to propel their application as pharmacophores and serves as an example of the mechanistic insight that can be gleaned from integrating data science and traditional physical organic techniques.
Data-driven reaction discovery and development is a growing field that relies on the use of molec... more Data-driven reaction discovery and development is a growing field that relies on the use of molecular descriptors to capture key information about substrates, ligands, and targets. Broad adaptation of this strategy is hindered by the associated computational cost of descriptor calculation, especially when considering conformational flexibility. Descriptor libraries can be precomputed agnostic of application to reduce the computational burden of data-driven reaction development. However, as one often applies these models to evaluate novel hypothetical structures, it would be ideal to predict the descriptors of compounds on-the-fly. Herein, we report DFT-level descriptor libraries for conformational ensembles of 8528 carboxylic acids and 8172 alkyl amines towards this goal. Employing 2D and 3D graph neural network architectures trained on these libraries culminated in the development of predictive models for molecule-level descriptors, as well as the bond-and atom-level descriptors for the conserved reactive site (carboxylic acid or amine). The predictions were confirmed to be robust for an external validation set of medicinally-relevant carboxylic acids and alkyl amines. Additionally, a retrospective study correlating the rate of amide coupling reactions demonstrated the suitability of the predicted DFT-level descriptors for downstream applications. Ultimately, these models enable high-fidelity predictions for a vast number of potential substrates, greatly increasing accessibility to the field of data-driven reaction development.
From the preparation of pharmaceuticals to enzymatic construction of natural products, carbocatio... more From the preparation of pharmaceuticals to enzymatic construction of natural products, carbocations are central to molecular synthesis. Although these reactive intermediates are engaged in stereoselective processes in nature, exerting enantiocontrol over carbocations with synthetic catalysts remains challenging. Many resonance-stabilized tricoordinated carbocations, such as iminium and oxocarbenium ions, have been applied in catalytic enantioselective reactions. However, their dicoordinated counterparts (aryl and vinyl carbocations) have not, despite their emerging utility in chemical synthesis. We report the discovery of a highly enantioselective vinyl carbocation carbon–hydrogen (C–H) insertion reaction enabled by imidodiphosphorimidate organocatalysts. Active site confinement featured in this catalyst class not only enables effective enantiocontrol but also expands the scope of vinyl cation C–H insertion chemistry, which broadens the utility of this transition metal–free C(sp 3 )...
Significance Given the ubiquity of amide coupling reactions, understanding the factors which infl... more Significance Given the ubiquity of amide coupling reactions, understanding the factors which influence the success of the reaction and having means to predict the reaction rate would streamline synthetic efforts. This study outlines a data science–based workflow for effective statistical modeling with sparse experimental data. We demonstrated informed substrate selection, collection of rate data and interpretable molecular descriptors, and statistical model development for amide coupling rates. The resulting statistical models illuminate substrate features that impact rate and allow for the prediction of untested amide coupling rates.
Methods to access chiral sulfur (VI) pharmacophores are of interest in medicinal and synthetic ch... more Methods to access chiral sulfur (VI) pharmacophores are of interest in medicinal and synthetic chemistry. We report the desymmetrization of unprotected sulfonimidamides via asymmetric acylation with a cinchona-phosphinate catalyst. The desired products are formed in excellent yield and enantioselectivity with no observed bis-acylation. A data science-driven approach to substrate scope evaluation was coupled to high throughput experimentation (HTE) to facilitate statistical modeling in order to inform mechanistic studies. Reaction kinetics, catalyst structural studies, and density functional theory (DFT) transition state analysis elucidated the turnover-limiting step to be the collapse of the tetrahedral intermediate and provided key insights into the catalyst-substrate structure-activity relationships responsible for the origin of enantioselectivity. This study offers a reliable method for accessing enantioenriched sulfonimidamides to propel their application as pharmacophores and serves as an example of the mechanistic insight that can be gleaned from integrating data science and traditional physical organic techniques.
Data-driven reaction discovery and development is a growing field that relies on the use of molec... more Data-driven reaction discovery and development is a growing field that relies on the use of molecular descriptors to capture key information about substrates, ligands, and targets. Broad adaptation of this strategy is hindered by the associated computational cost of descriptor calculation, especially when considering conformational flexibility. Descriptor libraries can be precomputed agnostic of application to reduce the computational burden of data-driven reaction development. However, as one often applies these models to evaluate novel hypothetical structures, it would be ideal to predict the descriptors of compounds on-the-fly. Herein, we report DFT-level descriptor libraries for conformational ensembles of 8528 carboxylic acids and 8172 alkyl amines towards this goal. Employing 2D and 3D graph neural network architectures trained on these libraries culminated in the development of predictive models for molecule-level descriptors, as well as the bond-and atom-level descriptors for the conserved reactive site (carboxylic acid or amine). The predictions were confirmed to be robust for an external validation set of medicinally-relevant carboxylic acids and alkyl amines. Additionally, a retrospective study correlating the rate of amide coupling reactions demonstrated the suitability of the predicted DFT-level descriptors for downstream applications. Ultimately, these models enable high-fidelity predictions for a vast number of potential substrates, greatly increasing accessibility to the field of data-driven reaction development.
From the preparation of pharmaceuticals to enzymatic construction of natural products, carbocatio... more From the preparation of pharmaceuticals to enzymatic construction of natural products, carbocations are central to molecular synthesis. Although these reactive intermediates are engaged in stereoselective processes in nature, exerting enantiocontrol over carbocations with synthetic catalysts remains challenging. Many resonance-stabilized tricoordinated carbocations, such as iminium and oxocarbenium ions, have been applied in catalytic enantioselective reactions. However, their dicoordinated counterparts (aryl and vinyl carbocations) have not, despite their emerging utility in chemical synthesis. We report the discovery of a highly enantioselective vinyl carbocation carbon–hydrogen (C–H) insertion reaction enabled by imidodiphosphorimidate organocatalysts. Active site confinement featured in this catalyst class not only enables effective enantiocontrol but also expands the scope of vinyl cation C–H insertion chemistry, which broadens the utility of this transition metal–free C(sp 3 )...
Significance Given the ubiquity of amide coupling reactions, understanding the factors which infl... more Significance Given the ubiquity of amide coupling reactions, understanding the factors which influence the success of the reaction and having means to predict the reaction rate would streamline synthetic efforts. This study outlines a data science–based workflow for effective statistical modeling with sparse experimental data. We demonstrated informed substrate selection, collection of rate data and interpretable molecular descriptors, and statistical model development for amide coupling rates. The resulting statistical models illuminate substrate features that impact rate and allow for the prediction of untested amide coupling rates.
Uploads
Papers by Brittany Haas