{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T03:57:32Z","timestamp":1773892652797,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000601","name":"De Montfort University","doi-asserted-by":"publisher","award":["VC2020 new staff L SL 2020"],"award-info":[{"award-number":["VC2020 new staff L SL 2020"]}],"id":[{"id":"10.13039\/501100000601","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model that is able to achieve better results than other models for relevant datasets with comparatively low amounts of data. We show this by predicting <jats:inline-formula><jats:alternatives><jats:tex-math>$$^{19}F$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:msup>\n                      <mml:mrow\/>\n                      <mml:mn>19<\/mml:mn>\n                    <\/mml:msup>\n                    <mml:mi>F<\/mml:mi>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and <jats:inline-formula><jats:alternatives><jats:tex-math>$$^{13}C$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:msup>\n                      <mml:mrow\/>\n                      <mml:mn>13<\/mml:mn>\n                    <\/mml:msup>\n                    <mml:mi>C<\/mml:mi>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> NMR chemical shifts of small molecules in specific solvents.<\/jats:p>\n                <jats:p><jats:bold>Graphical Abstract<\/jats:bold><\/jats:p>","DOI":"10.1186\/s13321-023-00785-x","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T13:02:33Z","timestamp":1701090153000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["NMR shift prediction from small data quantities"],"prefix":"10.1186","volume":"15","author":[{"given":"Herman","family":"Rull","sequence":"first","affiliation":[]},{"given":"Markus","family":"Fischer","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Kuhn","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"issue":"15","key":"785_CR1","doi-asserted-by":"publisher","first-page":"3977","DOI":"10.1021\/ja01620a009","volume":"77","author":"BP Dailey","year":"1955","unstructured":"Dailey BP, Shoolery JN (1955) The electron withdrawal power of substituent groups. J Am Chem Soc 77(15):3977\u20133981. https:\/\/doi.org\/10.1021\/ja01620a009","journal-title":"J Am Chem Soc"},{"issue":"6","key":"785_CR2","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci00010a023","volume":"32","author":"V Kvasnicka","year":"1992","unstructured":"Kvasnicka V, Sklenak S, Pospichal J (1992) Application of recurrent neural networks in chemistry. prediction and classification of carbon-13 NMR chemical shifts in a series of monosubstituted benzenes. J Chem Inf Comput Sci 32(6):742\u2013747. https:\/\/doi.org\/10.1021\/ci00010a023","journal-title":"J Chem Inf Comput Sci"},{"issue":"11","key":"785_CR3","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1002\/mrc.5234","volume":"60","author":"E Jonas","year":"2022","unstructured":"Jonas E, Kuhn S, Schl\u00f6rer N (2022) Prediction of chemical shift in NMR: a review. Magnetic resonance in chemistry\u202f: MRC 60(11):1021\u20131031. https:\/\/doi.org\/10.1002\/mrc.5234","journal-title":"Magnetic resonance in chemistry : MRC"},{"issue":"41","key":"785_CR4","doi-asserted-by":"publisher","first-page":"7487","DOI":"10.1021\/acs.orglett.2c01251","volume":"24","author":"Y-H Tsai","year":"2022","unstructured":"Tsai Y-H, Amichetti M, Zanardi MM, Grimson R, Daranas AH, Sarotti AM (2022) ML-J-DP4: An integrated quantum mechanics-machine learning approach for ultrafast NMR structural elucidation. Org Lett 24(41):7487\u20137491. https:\/\/doi.org\/10.1021\/acs.orglett.2c01251. (PMID: 35508069)","journal-title":"Org Lett"},{"issue":"1","key":"785_CR5","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/s13321-019-0374-3","volume":"11","author":"E Jonas","year":"2019","unstructured":"Jonas E, Kuhn S (2019) Rapid prediction of NMR spectral properties with quantified uncertainty. J Cheminform 11(1):50","journal-title":"J Cheminform"},{"issue":"2","key":"785_CR6","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1021\/acs.jctc.0c00979","volume":"17","author":"PA Unzueta","year":"2021","unstructured":"Unzueta PA, Greenwell CS, Beran GJO (2021) Predicting Density Functional Theory-Quality Nuclear Magnetic Resonance Chemical Shifts via $$\\Delta$$-Machine Learning. J Chem Theory Comput 17(2):826\u2013840","journal-title":"J Chem Theory Comput"},{"issue":"4","key":"785_CR7","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1021\/acs.jcim.0c00195","volume":"60","author":"Y Kwon","year":"2020","unstructured":"Kwon Y, Lee D, Choi Y-S, Kang M, Kang S (2020) Neural message passing for NMR chemical shift prediction. J Chem Inf Model 60(4):2024\u20132030. https:\/\/doi.org\/10.1021\/acs.jcim.0c00195. (PMID: 32250618)","journal-title":"J Chem Inf Model"},{"key":"785_CR8","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1039\/C9SC03854J","volume":"11","author":"W Gerrard","year":"2020","unstructured":"Gerrard W, Bratholm LA, Packer MJ, Mulholland AJ, Glowacki DR, Butts CP (2020) Impression\u2014prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy. Chem Sci 11:508\u2013515. https:\/\/doi.org\/10.1039\/C9SC03854J","journal-title":"Chem Sci"},{"key":"785_CR9","unstructured":"Modgraph Consultants Ltd (2023) NMR Predict Desktop. https:\/\/www.modgraph.co.uk\/product_nmr_desktop.htm. Accessed 24 Feb 2023"},{"key":"785_CR10","doi-asserted-by":"publisher","first-page":"12012","DOI":"10.1039\/D1SC03343C","volume":"12","author":"Y Guan","year":"2021","unstructured":"Guan Y, Shree\u00a0Sowndarya S.V, Gallegos L.C., St.\u00a0John P.C, Paton R.S (2021) Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network. Chem Sci 12:12012\u201312026. https:\/\/doi.org\/10.1039\/D1SC03343C","journal-title":"Chem Sci"},{"key":"785_CR11","doi-asserted-by":"publisher","unstructured":"Kuhn S, Borges RM, Venturini F, Sansotera M (2022) Dataset size and machine learning\u2014open nmr databases as a case study. In: 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). p 1632\u20131636 . https:\/\/doi.org\/10.1109\/COMPSAC54236.2022.00259","DOI":"10.1109\/COMPSAC54236.2022.00259"},{"key":"785_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.pnmrs.2015.02.002","volume":"86\u201387","author":"K Zangger","year":"2015","unstructured":"Zangger K (2015) Pure shift NMR. Prog Nucl Magn Reson Spectrosc 86\u201387:1\u201320. https:\/\/doi.org\/10.1016\/j.pnmrs.2015.02.002","journal-title":"Prog Nucl Magn Reson Spectrosc"},{"key":"785_CR13","doi-asserted-by":"publisher","unstructured":"Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, Gulcehre C, Song F, Ballard A, Gilmer J, Dahl G, Vaswani A, Allen K, Nash C, Langston V, Dyer C, Heess N, Wierstra D, Kohli P, Botvinick M, Vinyals O, Li Y, Pascanu R (2018) Relational inductive biases, deep learning, and graph networks. arXiv . https:\/\/doi.org\/10.48550\/ARXIV.1806.01261","DOI":"10.48550\/ARXIV.1806.01261"},{"key":"785_CR14","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1109\/IJCNN.2005.1555942","volume":"2","author":"M Gori","year":"2005","unstructured":"Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. Proc 2005 IEEE Int Joint Conf Neural Netw 2:729\u20137342. https:\/\/doi.org\/10.1109\/IJCNN.2005.1555942","journal-title":"Proc 2005 IEEE Int Joint Conf Neural Netw"},{"key":"785_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2022.107750","volume":"100","author":"M Fischer","year":"2022","unstructured":"Fischer M, Schwarze B, Ristic N, Scheidt HA (2022) Predicting 2H NMR acyl chain order parameters with graph neural networks. Comput Biol Chem 100:107750. https:\/\/doi.org\/10.1016\/j.compbiolchem.2022.107750","journal-title":"Comput Biol Chem"},{"key":"785_CR16","doi-asserted-by":"publisher","unstructured":"Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, G\u00f3mez-Bombarelli R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. arXiv . https:\/\/doi.org\/10.48550\/ARXIV.1509.09292 . https:\/\/arxiv.org\/abs\/1509.09292","DOI":"10.48550\/ARXIV.1509.09292"},{"key":"785_CR17","doi-asserted-by":"publisher","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. arXiv . https:\/\/doi.org\/10.48550\/ARXIV.1704.01212","DOI":"10.48550\/ARXIV.1704.01212"},{"key":"785_CR18","unstructured":"RDKit (2023) Open-source cheminformatics. https:\/\/www.rdkit.org. Accessed 24 Feb 2023"},{"key":"785_CR19","unstructured":"mendeleev (2014) A Python resource for properties of chemical elements, ions and isotopes, ver. 0.12.1. https:\/\/github.com\/lmmentel\/mendeleev. Accessed 24 Feb 2023"},{"issue":"4","key":"785_CR20","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/S0003-2670(01)83100-7","volume":"103","author":"W Bremser","year":"1978","unstructured":"Bremser W (1978) Hose: a novel substructure code. Anal Chim Acta 103(4):355\u2013365. https:\/\/doi.org\/10.1016\/S0003-2670(01)83100-7","journal-title":"Anal Chim Acta"},{"issue":"1","key":"785_CR21","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s13321-017-0220-4","volume":"9","author":"EL Willighagen","year":"2017","unstructured":"Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chert\u00f3 M, Spjuth O, Torrance G, Evelo CT, Guha R, Steinbeck C (2017) The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform 9(1):33","journal-title":"J Cheminform"},{"key":"785_CR22","unstructured":"Generating HOSE codes of molecules with Python (2023) https:\/\/github.com\/Ratsemaat\/HOSE_code_generator. Accessed 24 Feb 2023"},{"issue":"4","key":"785_CR23","doi-asserted-by":"publisher","first-page":"7323","DOI":"10.1021\/acsomega.9b00488","volume":"4","author":"S Kuhn","year":"2019","unstructured":"Kuhn S, Johnson SR (2019) Stereo-aware extension of HOSE codes. ACS Omega 4(4):7323\u20137329","journal-title":"ACS Omega"},{"issue":"1","key":"785_CR24","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/1758-2946-4-S1-P52","volume":"4","author":"S Kuhn","year":"2012","unstructured":"Kuhn S, Schl\u00f6rer NE, Kolshorn H, Stoll R (2012) From chemical shift data through prediction to assignment and NMR LIMS-multiple functionalities of nmrshiftdb2. J Cheminf 4(1):52. https:\/\/doi.org\/10.1186\/1758-2946-4-S1-P52","journal-title":"J Cheminf"},{"key":"785_CR25","unstructured":"Simon E (2023) Mapping chemical space with UMAP. https:\/\/gist.github.com\/ElanaPearl\/444b3331f61485bbe8862db27cb2b968. Accessed 8 Mar 2023"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00785-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-023-00785-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00785-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T13:10:17Z","timestamp":1701090617000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-023-00785-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["785"],"URL":"https:\/\/doi.org\/10.1186\/s13321-023-00785-x","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"16 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"114"}}