Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts
Abstract This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosp...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-12-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13321-023-00792-y |
| _version_ | 1797376827114651648 |
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| author | Jasmin Hack Moritz Jordan Alina Schmitt Melissa Raru Hannes Sönke Zorn Alex Seyfarth Isabel Eulenberger Robert Geitner |
| author_facet | Jasmin Hack Moritz Jordan Alina Schmitt Melissa Raru Hannes Sönke Zorn Alex Seyfarth Isabel Eulenberger Robert Geitner |
| author_sort | Jasmin Hack |
| collection | DOAJ |
| description | Abstract This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for 31P NMR shift prediction, showcasing the database’s potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations. |
| first_indexed | 2024-03-08T19:44:13Z |
| format | Article |
| id | doaj.art-2f712febe37e42298fa071bb2cfaa152 |
| institution | Directory Open Access Journal |
| issn | 1758-2946 |
| language | English |
| last_indexed | 2024-03-08T19:44:13Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj.art-2f712febe37e42298fa071bb2cfaa1522023-12-24T12:27:49ZengBMCJournal of Cheminformatics1758-29462023-12-0115111210.1186/s13321-023-00792-yIlm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shiftsJasmin Hack0Moritz Jordan1Alina Schmitt2Melissa Raru3Hannes Sönke Zorn4Alex Seyfarth5Isabel Eulenberger6Robert Geitner7Institute of Chemistry and Bioengineering, Group of Physical Chemistry/Catalysis, Technical University IlmenauInstitute of Chemistry and Bioengineering, Group of Physical Chemistry/Catalysis, Technical University IlmenauInstitute of Chemistry and Bioengineering, Group of Physical Chemistry/Catalysis, Technical University IlmenauInstitute of Chemistry and Bioengineering, Group of Physical Chemistry/Catalysis, Technical University IlmenauInstitute of Chemistry and Bioengineering, Group of Physical Chemistry/Catalysis, Technical University IlmenauInstitute of Chemistry and Bioengineering, Group of Physical Chemistry/Catalysis, Technical University IlmenauInstitute of Chemistry and Bioengineering, Group of Physical Chemistry/Catalysis, Technical University IlmenauInstitute of Chemistry and Bioengineering, Group of Physical Chemistry/Catalysis, Technical University IlmenauAbstract This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for 31P NMR shift prediction, showcasing the database’s potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations.https://doi.org/10.1186/s13321-023-00792-yNMRPhosphorusDatabasePredictionIncrement systemFingerprint |
| spellingShingle | Jasmin Hack Moritz Jordan Alina Schmitt Melissa Raru Hannes Sönke Zorn Alex Seyfarth Isabel Eulenberger Robert Geitner Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts Journal of Cheminformatics NMR Phosphorus Database Prediction Increment system Fingerprint |
| title | Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts |
| title_full | Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts |
| title_fullStr | Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts |
| title_full_unstemmed | Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts |
| title_short | Ilm-NMR-P31: an open-access 31P nuclear magnetic resonance database and data-driven prediction of 31P NMR shifts |
| title_sort | ilm nmr p31 an open access 31p nuclear magnetic resonance database and data driven prediction of 31p nmr shifts |
| topic | NMR Phosphorus Database Prediction Increment system Fingerprint |
| url | https://doi.org/10.1186/s13321-023-00792-y |
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