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Iminoiodinanes comprise a class of hypervalent iodine reagents that is often encountered in nitrogen-group transfer (NGT) catalysis. In general, transition metal catalysts are required to effect efficient NGT to unactivated olefins because iminoiodinanes are insufficiently electrophilic to engage in direct aziridination chemistry. Here, we demonstrate that 1,1,1,3,3,3-hexafluoroisopropanol (HFIP) activatesN-arylsulfonamide-derived iminoiodinanes for the metal-free aziridination of unactivated olefins.¹H NMR and cyclic voltammetry (CV) studies indicate that hydrogen-bonding between HFIP and the iminoiodinane generates an oxidant capable of direct NGT to unactivated olefins. Stereochemical scrambling during aziridination of 1,2-disubstituted olefins is observed and interpreted as evidence that aziridination proceeds via a carbocation intermediate that subsequently cyclizes. These results demonstrate a simple method for activating iminoiodinane reagents, provide analysis of the extent of activation achieved by H-bonding, and indicate the potential for chemical non-innocence of fluorinated alcohol solvents in NGT catalysis. 

Abstract The design and optimization of novel electrocatalysts requires robust structure–activity data to correlate catalyst structure with electrochemical behavior. Aryl iodides have been gaining attention as metal-free electrocatalysts but experimental data are available for only a limited set of structures. Herein we report electrochemical data for a family of 70 aryl iodides. Half-peak potentials are utilized as proxies for reduction potentials and reveal that, despite differences in electrochemical reversibility, the potential for one-electron oxidation of 4-substituted aryl iodides to the corresponding iodanyl radicals is well-correlated with standard Hammett parameters. Additional data are presented for 3- and 2-substituted aryl iodides, including structures with potentially chelating 2-substituents that are commonly encountered in hypervalent iodine reagents. Finally, potential decomposition processes relevant to the (in)stability of iodanyl radicals are presented. We anticipate that the collected data will advance the design and application of aryl iodide electrocatalysis. 

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.