文章基本信息

标题：Rapid prediction of NMR spectral properties with quantified uncertainty
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作者：Eric Jonas ; Stefan Kuhn
期刊名称：Journal of Cheminformatics
印刷版ISSN：1758-2946
电子版ISSN：1758-2946
出版年度：2019
卷号：11
期号：1
页码：1-7
DOI：10.1186/s13321-019-0374-3
出版社：BioMed Central
摘要：Accurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation. Here we report the development of a novel machine learning technique for accurately predicting chemical shifts of both $${^1\mathrm{H}}$$ and $${^{13}\mathrm{C}}$$ nuclei which exceeds DFT-accessible accuracy for $${^{13}\mathrm{C}}$$ and $${^1\mathrm{H}}$$ for a subset of nuclei, while being orders of magnitude more performant. Our method produces estimates of uncertainty, allowing for robust and confident predictions, and suggests future avenues for improved performance.
关键词：NMR; Machine learning; DFT