Dataset Design for Building Models of Chemical Reactivity

Raghavan, Priyanka; Haas, Brittany C.; Ruos, Madeline E.; Schleinitz, Jules; Doyle, Abigail G.; Reisman, Sarah E.; Sigman, Matthew S.; Coley, Connor W.

doi:10.1021/acscentsci.3c01163

Cited by 17 publications

(15 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, substrates containing heteroaromatic groups at more remote sites were well-tolerated (e.g., 18 , 20 , and 26 ). Similar observations have been reported in the literature for related transformations. , These results highlight that the reactivity of more complex substrates cannot be readily extrapolated from simple substrates or additive screens. ,− …”

Section: Resultssupporting

confidence: 86%

Development of High-Throughput Experimentation Approaches for Rapid Radiochemical Exploration

Webb,

Cheng,

Winton

et al. 2024

J. Am. Chem. Soc.

View full text Add to dashboard Cite

Positron emission tomography is a widely used imaging platform for studying physiological processes. Despite the proliferation of modern synthetic methodologies for radiolabeling, the optimization of these reactions still primarily relies on inefficient one-factor-at-a-time approaches. High-throughput experimentation (HTE) has proven to be a powerful approach for optimizing reactions in many areas of chemical synthesis. However, to date, HTE has rarely been applied to radiochemistry. This is largely because of the short lifetime of common radioisotopes, which presents major challenges for efficient parallel reaction setup and analysis using standard equipment and workflows. Herein, we demonstrate an effective HTE workflow and apply it to the optimization of copper-mediated radiofluorination of pharmaceutically relevant boronate ester substrates. The workflow utilizes commercial equipment and allows for rapid analysis of reactions for optimizing reactions, exploring chemical space using pharmaceutically relevant aryl boronates for radiofluorinations, and constructing large radiochemistry data sets.

show abstract

Section: Resultssupporting

confidence: 86%

Development of High-Throughput Experimentation Approaches for Rapid Radiochemical Exploration

Webb,

Cheng,

Winton

et al. 2024

J. Am. Chem. Soc.

View full text Add to dashboard Cite

show abstract

“…As the first stage, we designed an HTE screening campaign that assessed a combinatorial matrix of catalysts and substrates. The resultant quantity and diversity of data would allow for the construction of enantioselectivity correlations with substrate and catalyst structural features through statistical modeling. , By assessing a combinatorial matrix, we hypothesized that most general features required for effective asymmetric catalysis would be revealed and serve to seed further computational and mechanistic studies that are required to elucidate the mechanism of this reaction. Ten representative catalysts that sampled two points of modulation, the phosphinate and quinoline substituents, were selected as they showcased a diversity of responses in the initial optimization campaign.…”

Section: Resultsmentioning

confidence: 99%

Enantioselective Sulfonimidamide Acylation via a Cinchona Alkaloid-Catalyzed Desymmetrization: Scope, Data Science, and Mechanistic Investigation

Haas,

Lim,

Jermaks

et al. 2024

J. Am. Chem. Soc.

Self Cite

View full text Add to dashboard Cite

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 bisacylation. 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 the 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.

show abstract

“…In conclusion, the sizes, biases, and noise of data sets are all crucial for building ML models for chemical reactions. Previous reviews focus on the relation between model architectures and tasks and the design of the data set . However, not all researchers are capable of designing their private data for modeling.…”

Section: Mainmentioning

confidence: 99%

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective

Ding,

Qiang,

Chen

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, and discover new pathways for synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning models, it is imperative to derive robust and informative representations or engage in feature engineering using extensive data sets of reactions. This work aims to provide a comprehensive review of established reaction featurization approaches, offering insights into the selection of representations and the design of features for a wide array of tasks. The advantages and limitations of employing SMILES, molecular fingerprints, molecular graphs, and physics-based properties are meticulously elaborated. Solutions to bridge the gap between different representations will also be critically evaluated. Additionally, we introduce a new frontier in chemical reaction pretraining, holding promise as an innovative yet unexplored avenue.

show abstract

Dataset Design for Building Models of Chemical Reactivity

Cited by 17 publications

References 55 publications

Development of High-Throughput Experimentation Approaches for Rapid Radiochemical Exploration

Development of High-Throughput Experimentation Approaches for Rapid Radiochemical Exploration

Enantioselective Sulfonimidamide Acylation via a Cinchona Alkaloid-Catalyzed Desymmetrization: Scope, Data Science, and Mechanistic Investigation

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective

Contact Info

Product

Resources

About