Database Independent Automated Structure Elucidation of Organic Molecules Based on IR, ¹H NMR, ¹³C NMR, and MS Data

Abstract

Herein, we report a computational algorithm that follows a spectroscopist-driven elucidation process of the structure of an organic molecule based on IR, 1 H and 13 C NMR, and MS tabular data. The algorithm is independent from database searching and is based on a bottom-up approach, building the molecular structure from small structural fragments visible in spectra. It employs an analytical combinatorial approach with a graph search technique to determine the connectivity of structural fragments that is based on the analysis of the NMR spectra, to connect the identified structural fragments into a molecular structure. After the process is completed, the interface lists the compound candidates, which are visualized by the WolframAlpha computational knowledge engine within the interface. The candidates are ranked according to the predefined rules for analyzing the spectral data. The developed elucidator has a user-friendly web interface and is publicly available ( ).

Identifying molecular functional groups of organic compounds by deep learning of NMR data

“…The issue of solving structures of proton‐deficient molecules has not yet been fully addressed by CASE programs. To overcome it, a newly chemical structure elucidator based on bottom‐up strategy is reported, which mimics a spectroscopist‐driven process of elucidating the molecular structure from infrared (IR), H & C NMR, and MS spectra [19] . Nevertheless, the algorithm currently only processes H NMR spectra with approximately first‐order spin coupling and is easily prone to failure when an expected peak corresponding to a specific functional group is missing on the spectra (IR or NMR).…”

A framework for automated structure elucidation from routine NMR spectra

“…However, these programs still require a large amount of human intervention to pick out the relevant peaks from complex 2D NMR spectra 19,20 . Another recently developed method uses a bottomup rule-based approach to solve molecular structure from a combination of tabulated infrared (IR) spectroscopy peaks, 1 H and 13 C NMR peaks, and mass spectra 21 . This approach requires picking 1 H NMR peaks and their multiplicities, which varies based on user interpretation, and is prone to failure if even a single expected peak is missing.…”

“… 19,20 Another recently developed method uses a bottom-up rule-based approach to solve molecular structure from a combination of tabulated infrared (IR) spectroscopy peaks, 1 H and 13 C NMR peaks, and mass spectra. 21 This approach requires picking 1 H NMR peaks and their multiplicities, which varies based on user interpretation, and is prone to failure if even a single expected peak is missing. Other approaches to automating NMR interpretation leverage the use of NMR databases.…”

Incorporation of 4J-HMBC and NOE Data into Computer-Assisted Structure Elucidation with WebCocon

“…Over the decades, many different methods have been implemented, the most prominent being fragment assemblers [ 1 , 2 , 3 , 4 , 5 , 6 ], expert systems [ 7 , 8 , 9 ], structure generation by reduction [ 10 ], logic engines [ 11 ], stochastic structure generators [ 12 ], combinatorial brute force [ 13 , 14 , 15 , 16 , 17 ], databases of C NMR chemical shifts and fragments [ 18 , 19 ], combinatorial structure generation with restraints [ 20 , 21 ], genetic algorithms [ 22 , 23 ], simulated annealing [ 24 ], convergent structure generation [ 25 , 26 ], evolutionary algorithm [ 27 ], fuzzy structure generation [ 28 ], and expert systems with DFT [ 29 ]. However, CASE remains a challenge [ 29 , 30 , 31 , 32 , 33 , 34 ].…”