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利用人工神经网络快速测定13C核磁共振化学位移

Fast determination of 13C NMR chemical shifts using artificial neural networks.

作者信息

Meiler J, Meusinger R, Will M

机构信息

Institute of Organic Chemistry, University of Frankfurt, Germany.

出版信息

J Chem Inf Comput Sci. 2000 Sep-Oct;40(5):1169-76. doi: 10.1021/ci000021c.

Abstract

Nine different artificial neural networks were trained with the spherically encoded chemical environments of more than 500000 carbon atoms to predict their 13C NMR chemical shifts. Based on these results the PC-program "C_shift" was developed which allows the calculation of the 13C NMR spectra of any proposed molecular structure consisting of the covalently bonded elements C, H, N, O, P, S and the halogens. Results were obtained with a mean deviation as low as 1.8 ppm; this accuracy is equivalent to a determination on the basis of a large database but, in a time as short as known from increment calculations, was demonstrated exemplary using the natural agent epothilone A. The artificial neural networks allow simultaneously a precise and fast prediction of a large number of 13C NMR spectra, as needed for high throughput NMR and screening of a substance or spectra libraries.

摘要

使用超过500000个碳原子的球形编码化学环境训练了9种不同的人工神经网络,以预测它们的13C NMR化学位移。基于这些结果,开发了PC程序“C_shift”,该程序可以计算任何由共价键合元素C、H、N、O、P、S和卤素组成的分子结构的13C NMR光谱。得到的结果平均偏差低至1.8 ppm;这种准确性相当于基于大型数据库的测定,但在增量计算所需的短时间内,使用天然药物埃坡霉素A进行了示例性证明。人工神经网络能够同时精确且快速地预测大量13C NMR光谱,这是高通量NMR以及物质或光谱库筛选所需要的。

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