Skeletal editing has emerged as a powerful tool in organic chemistry,enabling the simplification of synthetic routes to complex molecules[1].Indoles,electron-rich nitrogen-containing building blocks,represent privileg...Skeletal editing has emerged as a powerful tool in organic chemistry,enabling the simplification of synthetic routes to complex molecules[1].Indoles,electron-rich nitrogen-containing building blocks,represent privileged scaffolds prevalent in pharmaceuticals,natural products,and bioactive compounds.The application of skeletal editing strategies to modify such structures is highly valuable and in growing demand.Leveraging the electronrich nature of indoles at C2 and C3,single-carbon atom insertion using cationic carbyne equivalents offers an efficient approach for indole ring expansion to quinoline(Scheme 1a).However,existing methods predominantly rely on halocarbene precursors,which restricts the functional groups of ring-expanded products to halogen[2],alkyl,aryl,heteroaryl and ester moieties[3].This limitation hinders their utility in late-stage skeletal modifications of complex targets.展开更多
An interactive web tool,PredPotS,has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions.The predictions are generated using deep learning models trained ...An interactive web tool,PredPotS,has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions.The predictions are generated using deep learning models trained and validated on a chemically diverse database comprising reduction potentials of approximately 8000 organic compounds.The reduction potentials of this database were computed using a composite computational protocol that combines the semiempirical quantum chemical method(GFN2-xTB)and awell-established DFT approach(M06-2X functional along with the SMD solvent model).While this computational approach is cost-effective,it is subject to certain limitations,which are nonetheless duly accounted for in the development of the database.The applied graph-based deep learning methods perform remarkably well in terms of the standard performance metrics.By entering or uploading the SMILES codes of the molecules,PredPotS provides fast and sensible predictions for one-electron standard reduction potentials for a diverse set of organic molecules also in the range compatible with the electrochemical stability of aqueous electrolytes.The PredPotS web tool is particularly well-suited for screening redox-active candidates for aqueous organic redox flow batteries,but it may also prove useful in a variety of other electrochemical applications.展开更多
文摘Skeletal editing has emerged as a powerful tool in organic chemistry,enabling the simplification of synthetic routes to complex molecules[1].Indoles,electron-rich nitrogen-containing building blocks,represent privileged scaffolds prevalent in pharmaceuticals,natural products,and bioactive compounds.The application of skeletal editing strategies to modify such structures is highly valuable and in growing demand.Leveraging the electronrich nature of indoles at C2 and C3,single-carbon atom insertion using cationic carbyne equivalents offers an efficient approach for indole ring expansion to quinoline(Scheme 1a).However,existing methods predominantly rely on halocarbene precursors,which restricts the functional groups of ring-expanded products to halogen[2],alkyl,aryl,heteroaryl and ester moieties[3].This limitation hinders their utility in late-stage skeletal modifications of complex targets.
基金funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875565 (CompBat project).
文摘An interactive web tool,PredPotS,has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions.The predictions are generated using deep learning models trained and validated on a chemically diverse database comprising reduction potentials of approximately 8000 organic compounds.The reduction potentials of this database were computed using a composite computational protocol that combines the semiempirical quantum chemical method(GFN2-xTB)and awell-established DFT approach(M06-2X functional along with the SMD solvent model).While this computational approach is cost-effective,it is subject to certain limitations,which are nonetheless duly accounted for in the development of the database.The applied graph-based deep learning methods perform remarkably well in terms of the standard performance metrics.By entering or uploading the SMILES codes of the molecules,PredPotS provides fast and sensible predictions for one-electron standard reduction potentials for a diverse set of organic molecules also in the range compatible with the electrochemical stability of aqueous electrolytes.The PredPotS web tool is particularly well-suited for screening redox-active candidates for aqueous organic redox flow batteries,but it may also prove useful in a variety of other electrochemical applications.
