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- W4223538076 abstract "Determination of the three-dimensional atomic-level structure of powdered solids is one of the key goals in current chemistry. Solid-state NMR chemical shifts can be used to solve this problem, but they are limited by the high computational cost associated with crystal structure prediction methods and density functional theory chemical shift calculations. Here, we successfully determine the crystal structures of ampicillin, piroxicam, cocaine, and two polymorphs of the drug molecule AZD8329 using on-the-fly generated machine-learned isotropic chemical shifts to directly guide a Monte Carlo-based structure determination process starting from a random gas-phase conformation." @default.
- W4223538076 created "2022-04-15" @default.
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- W4223538076 date "2022-04-13" @default.
- W4223538076 modified "2023-09-30" @default.
- W4223538076 title "<i>De Novo</i> Crystal Structure Determination from Machine Learned Chemical Shifts" @default.
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- W4223538076 doi "https://doi.org/10.1021/jacs.1c13733" @default.
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