Article
Version 1
Preserved in Portico This version is not peer-reviewed
Can Graph Machines Accurately Estimate 13C NMR Chemical Shifts of Benzenic Compounds?
Version 1
: Received: 7 June 2024 / Approved: 7 June 2024 / Online: 7 June 2024 (15:07:53 CEST)
A peer-reviewed article of this Preprint also exists.
Duprat, F.; Ploix, J.-L.; Dreyfus, G. Can Graph Machines Accurately Estimate 13C NMR Chemical Shifts of Benzenic Compounds? Molecules 2024, 29, 3137. Duprat, F.; Ploix, J.-L.; Dreyfus, G. Can Graph Machines Accurately Estimate 13C NMR Chemical Shifts of Benzenic Compounds? Molecules 2024, 29, 3137.
Abstract
NMR spectroscopy, which is based on the phenomenon of nuclear magnetic resonance, has been widely popularized by its application in medical imaging under the name of MRI. In the organic laboratory, the 13C NMR spectrum of a newly synthetized compound remains an essential step in elucidating its structure. For the chemist, the interpretation of such a spectrum, which is a set of chemical shift values, is made easier if he has a tool capable of predicting with sufficient accuracy the carbon shift values from the structure he intends to prepare. As there are few open source methods for accurately estimating this property, we applied our graph machine approach to build models capable of predicting the chemical shifts of carbons. For this study, we have focused on benzene compounds building an optimized model derived from training a database of 10577 chemical shifts originating from 2026 structures which contain up to ten types of atoms other than carbon, namely H, O, N, S, P, Si and halogens. It provides a training root mean squared relative error (RMSRE) of 0.5 %, i.e. a root mean squared error (RMSE) of 0.6 ppm, and a mean absolute error (MAE) of 0.4 ppm for estimating the chemical shifts of the 10k carbons. The predictive capability of the graph machine model is also compared with that of three commercial software on a data set of 171 original benzenic structures (1012 chemical shifts). The graph machine model proves very efficient in predicting chemical shifts with an RMSE of 0.9 ppm, and compares favorably with the RMSEs of 3.4, 1.8 and 1.9 ppm computed with ChemDraw, ACD and MestReNova softwares respectively. Finally, a Docker-based tool is proposed to predict the carbon chemical shifts of benzenic compounds solely from their SMILES codes.
Keywords
chemical shift; graph machines (GM); machine learning; structured data; Docker
Subject
Chemistry and Materials Science, Organic Chemistry
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
