Howarth Alexander, Ermanis Kristaps, Goodman Jonathan M
Centre for Molecular Informatics, Department of Chemistry, University of Cambridge Lensfield Road Cambridge CB2 1EW UK
Chem Sci. 2020 Mar 6;11(17):4351-4359. doi: 10.1039/d0sc00442a.
A robust system for automatic processing and assignment of raw C and H NMR data DP4-AI has been developed and integrated into our computational organic molecule structure elucidation workflow. Starting from a molecular structure with undefined stereochemistry or other structural uncertainty, this system allows for completely automated structure elucidation. Methods for NMR peak picking using objective model selection and algorithms for matching the calculated C and H NMR shifts to peaks in noisy experimental NMR data were developed. DP4-AI achieved a 60-fold increase in processing speed, and near-elimination of the need for scientist time, when rigorously evaluated using a challenging test set of molecules. DP4-AI represents a leap forward in NMR structure elucidation and a step-change in the functionality of DP4. It enables high-throughput analyses of databases and large sets of molecules, which were previously impossible, and paves the way for the discovery of new structural information through machine learning. This new functionality has been coupled with an intuitive GUI and is available as open-source software at https://github.com/KristapsE/DP4-AI.
一个用于自动处理和分配原始碳和氢核磁共振数据的强大系统DP4-AI已被开发出来,并集成到我们的计算有机分子结构解析工作流程中。从具有未定义立体化学或其他结构不确定性的分子结构开始,该系统允许完全自动化的结构解析。开发了使用客观模型选择进行核磁共振峰挑选的方法,以及将计算出的碳和氢核磁共振化学位移与嘈杂的实验核磁共振数据中的峰进行匹配的算法。当使用具有挑战性的分子测试集进行严格评估时,DP4-AI的处理速度提高了60倍,并且几乎不再需要科学家花费时间。DP4-AI代表了核磁共振结构解析的一大进步,以及DP4功能的一次重大变革。它能够对数据库和大量分子进行高通量分析,而这在以前是不可能的,并为通过机器学习发现新的结构信息铺平了道路。这种新功能与直观的图形用户界面相结合,并作为开源软件在https://github.com/KristapsE/DP4-AI上提供。