New Lignan from the Flowers of Forsythia koreana

Lee, Yeong‐Geun; Seo, Kyeong‐Hwa; Gwag, Jung Eun; Kim, Hyoung‐Geun; Ko, Jung‐Hwan; Lee, Dong Hoon; Baek, Nam‐In

doi:10.1007/s10600-019-02707-6

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…With 8 NP classes, the total number of test cases is then 40. The refs − report new NP elucidation of families that we are interested in (see Tables S10–S17). Six different XGBoost models were tested on these cases: five models with the hyperparameters that achieved the highest f1-scores while training with the different resampling techniques and a model that employed a different input representation.…”

Section: Test Casesmentioning

confidence: 99%

Prediction of Natural Product Classes Using Machine Learning and ¹³C NMR Spectroscopic Data

Martínez-Treviño¹,

Uc-Cetina

Fernández-Herrera³

et al. 2020

J. Chem. Inf. Model.

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Structure elucidation of chemical compounds is a complex and challenging activity that requires expertise and well-suited tools. To assign the molecular structure of a given compound, 13C NMR is one of the most widely used techniques because of its broad range of structural information. Taking into account that molecules found in nature can be grouped into natural product (NP) classes because of structural similarities, we explore the possibility of NP class prediction via 13C NMR data. Employing freely available 13C NMR data of NPs, we trained four classifiers for the prediction of eight common NP classes. The best performance was obtained with the XGBoost classifier reaching f1-scores of above 0.82. We also performed experiments with different percentages of positive samples, including the glycoside presence. Furthermore, we tested cases outside the data set, yielding performances above 80% for most classes. For the chromans case, we restricted the test examples to the coumarin subclass, and the prediction accuracy increased to 100%.

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Section: Test Casesmentioning

confidence: 99%

Prediction of Natural Product Classes Using Machine Learning and ¹³C NMR Spectroscopic Data

Martínez-Treviño¹,

Uc-Cetina

Fernández-Herrera³

et al. 2020

J. Chem. Inf. Model.

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LC-MS-based identification and antioxidant evaluation of small molecules from the cinnamon oil extraction waste

Yang

Al-Mahdy

et al. 2022

Food Chemistry

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New Lignan from the Flowers of Forsythia koreana

Cited by 2 publications

References 8 publications

Prediction of Natural Product Classes Using Machine Learning and ¹³C NMR Spectroscopic Data

Prediction of Natural Product Classes Using Machine Learning and ¹³C NMR Spectroscopic Data

LC-MS-based identification and antioxidant evaluation of small molecules from the cinnamon oil extraction waste

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New Lignan from the Flowers of Forsythia koreana

Cited by 2 publications

References 8 publications

Prediction of Natural Product Classes Using Machine Learning and 13C NMR Spectroscopic Data

Prediction of Natural Product Classes Using Machine Learning and 13C NMR Spectroscopic Data

LC-MS-based identification and antioxidant evaluation of small molecules from the cinnamon oil extraction waste

Contact Info

Product

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About

Prediction of Natural Product Classes Using Machine Learning and ¹³C NMR Spectroscopic Data

Prediction of Natural Product Classes Using Machine Learning and ¹³C NMR Spectroscopic Data