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Heteroaromatics are the basis for many pharmaceuticals. The ability to modify these structures through selective core-atom transformations, or “skeletal edits”, can dramatically expand the landscape for drug discovery and development. However, despite the importance of core-atom modifications, the quantitative impact of such transformations on accessible chemical space remains undefined. Here, we report a cheminformatic platform to analyze which skeletal edits would most increase access to novel chemical space. This study underscores the significance of emerging single and multiple core-atom transformations of heteroaromatics in enhancing chemical diversity, for example, at a late-stage of a drug discovery campaign. Our findings provide a quantitative framework for prioritizing core-atom modifications in heteroaromatic structural motifs, calling for the development of new methods to achieve these types of transformations. 

The application of statistical modeling in organic chemistry is emerging as a standard practice for probing structure-activity relationships and as a predictive tool for many optimization objectives. This review is aimed as a tutorial for those entering the area of statistical modeling in chemistry. We provide case studies to highlight the considerations and approaches that can be used to successfully analyze datasets in low data regimes, a common situation encountered given the experimental demands of organic chemistry. Statistical modeling hinges on the data (what is being modeled), descriptors (how data are represented), and algorithms (how data are modeled). Herein, we focus on how various reaction outputs (e.g., yield, rate, selectivity, solubility, stability, and turnover number) and data structures (e.g., binned, heavily skewed, and distributed) influence the choice of algorithm used for constructing predictive and chemically insightful statistical models. 

Abstract The term “generality” has recently been popularized in synthetic chemistry, owing largely to the increasing use of high‐throughput technology for producing vast quantities of data and the emergence of data science tools to plan and interpret these experiments. Despite this, the term has not been clearly defined, and there is no standardized approach toward developing a method with a diverse (general) scope. This minireview will examine different emerging strategies toward achieving generality using selected examples and aims to give the reader an overview of modern workflows that have been used to expedite this pursuit. 

DFT-level descriptor libraries were constructed to train 2D and 3D graph neural networks for on the-fly the prediction of carboxylic acid and alkyl amine descriptors suitable for statistical modeling of medicinally relevant molecules. 

Pd catalysis is used to convert electron-deficient (hetero)aryl sulfonyl fluorides to the corresponding (hetero)aryl fluorides with extrusion of SO₂.