Deciding which is the best 1H NMR predictor for organic compounds using statistical tools

1H NMR chemical shifts for 30 organic compounds (396 data points) were predicted using four NMR predictor software and compared with the experimental data from SDBS. The NMR predictors involved were MestReNova, ChemDraw, NMRShiftDB and ACD Workbook Suite. Root mean square deviation (RMSD) and mean a...

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Main Authors: Mah, W.H., Nazuan, N.H.A., Yeap, W.S., Fakharudin, F.H., Faye, I., Wilfred, C.D.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.32367 /
Published: Academie des sciences 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126594463&doi=10.5802%2fcrchim.156&partnerID=40&md5=b54c7530b581913bdd992c34800c001e
http://eprints.utp.edu.my/32367/
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spelling utp-eprints.323672022-03-28T13:50:36Z Deciding which is the best 1H NMR predictor for organic compounds using statistical tools Mah, W.H. Nazuan, N.H.A. Yeap, W.S. Fakharudin, F.H. Faye, I. Wilfred, C.D. 1H NMR chemical shifts for 30 organic compounds (396 data points) were predicted using four NMR predictor software and compared with the experimental data from SDBS. The NMR predictors involved were MestReNova, ChemDraw, NMRShiftDB and ACD Workbook Suite. Root mean square deviation (RMSD) and mean absolute percentage error (MAPE) were calculated from the data obtained. One-way analysis of variance (ANOVA), Tukey's honestly significant difference (HSD) and t -test were carried out to analyse the statistical significance of the differences between the predictors. The results from the statistical analysis were used to predict chemical shifts of three organic compounds. © 2022 Elsevier Masson SAS. All rights reserved. Academie des sciences 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126594463&doi=10.5802%2fcrchim.156&partnerID=40&md5=b54c7530b581913bdd992c34800c001e Mah, W.H. and Nazuan, N.H.A. and Yeap, W.S. and Fakharudin, F.H. and Faye, I. and Wilfred, C.D. (2022) Deciding which is the best 1H NMR predictor for organic compounds using statistical tools. Comptes Rendus Chimie, 25 . pp. 83-95. http://eprints.utp.edu.my/32367/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 1H NMR chemical shifts for 30 organic compounds (396 data points) were predicted using four NMR predictor software and compared with the experimental data from SDBS. The NMR predictors involved were MestReNova, ChemDraw, NMRShiftDB and ACD Workbook Suite. Root mean square deviation (RMSD) and mean absolute percentage error (MAPE) were calculated from the data obtained. One-way analysis of variance (ANOVA), Tukey's honestly significant difference (HSD) and t -test were carried out to analyse the statistical significance of the differences between the predictors. The results from the statistical analysis were used to predict chemical shifts of three organic compounds. © 2022 Elsevier Masson SAS. All rights reserved.
format Article
author Mah, W.H.
Nazuan, N.H.A.
Yeap, W.S.
Fakharudin, F.H.
Faye, I.
Wilfred, C.D.
spellingShingle Mah, W.H.
Nazuan, N.H.A.
Yeap, W.S.
Fakharudin, F.H.
Faye, I.
Wilfred, C.D.
Deciding which is the best 1H NMR predictor for organic compounds using statistical tools
author_sort Mah, W.H.
title Deciding which is the best 1H NMR predictor for organic compounds using statistical tools
title_short Deciding which is the best 1H NMR predictor for organic compounds using statistical tools
title_full Deciding which is the best 1H NMR predictor for organic compounds using statistical tools
title_fullStr Deciding which is the best 1H NMR predictor for organic compounds using statistical tools
title_full_unstemmed Deciding which is the best 1H NMR predictor for organic compounds using statistical tools
title_sort deciding which is the best 1h nmr predictor for organic compounds using statistical tools
publisher Academie des sciences
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126594463&doi=10.5802%2fcrchim.156&partnerID=40&md5=b54c7530b581913bdd992c34800c001e
http://eprints.utp.edu.my/32367/
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score 11.62408

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