Suppr超能文献

基于参数修正的 DFT 计算 13C NMR 化学位移和自旋-自旋偶合常数实现了卤代天然产物的高通量计算结构验证和修正。

High-Throughput in Silico Structure Validation and Revision of Halogenated Natural Products Is Enabled by Parametric Corrections to DFT-Computed C NMR Chemical Shifts and Spin-Spin Coupling Constants.

机构信息

Department of Chemistry and Biochemistry, University of Denver , Denver, Colorado 80208, United States.

出版信息

J Org Chem. 2017 Apr 7;82(7):3368-3381. doi: 10.1021/acs.joc.7b00188. Epub 2017 Mar 24.

Abstract

Halogenated natural products constitute diverse and promising feedstock for molecular pharmaceuticals. However, their solution-structure elucidation by NMR presents several challenges, including the lack of fast methods to compute C chemical shifts for carbons bearing heavy atoms. We show that parametric corrections to DFT-computed chemical shifts in conjunction with rff-computed spin-spin coupling constants allow for fast and reliable screening of a large number of reported halogenated natural products, resulting in expedient structure validation or revision. In this paper, we examine more than 100 structures of halogenated terpenoids and other natural products with the new parametric approach and demonstrate that the accuracy of the combined method is sufficient to identify misassignments and suggest revisions in most cases (16 structures are revised). As the 1D H and C NMR data are ubiquitous and most routinely used in solution structure elucidation, this fast and efficient two-criterion method (nuclear spin-spin coupling and C chemical shifts) which we term DU8+ is recommended as the first essential step in structure assignment and validation.

摘要

卤代天然产物是分子药物中具有多样性和广阔应用前景的原料。然而,通过 NMR 对其溶液结构进行解析存在一些挑战,其中包括缺乏计算含重原子碳的 C 化学位移的快速方法。我们表明,与 rff 计算的自旋-自旋耦合常数相结合,对 DFT 计算的化学位移进行参数修正,可快速可靠地筛选大量报道的卤代天然产物,从而快速有效地验证或修正结构。在本文中,我们使用新的参数方法对 100 多种卤代萜类化合物和其他天然产物的结构进行了研究,结果表明,组合方法的准确性足以在大多数情况下识别错误分配并提出修正建议(16 个结构被修正)。由于 1D H 和 C NMR 数据无处不在,并且在溶液结构解析中最常用,因此我们将这种快速有效的双标准方法(核自旋-自旋耦合和 C 化学位移)称为 DU8+,推荐作为结构分配和验证的第一个必要步骤。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验