{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:18:45Z","timestamp":1785406725121,"version":"3.56.0"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62262029"],"award-info":[{"award-number":["62262029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62262029"],"award-info":[{"award-number":["62262029"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20212BAB202016"],"award-info":[{"award-number":["20212BAB202016"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Accurate matching between molecular structures and NMR spectra is an important task in automated structure elucidation. However, existing methods still face difficulties in jointly modeling multi-scale molecular topology and effectively exploiting the complementary information provided by paired 1H and 13C NMR spectra. To address these limitations, we propose SpecMol-MatchNet, a multimodal matching framework that integrates a hybrid molecular graph encoder, branch-specific spectral feature learning, and residual multimodal fusion. In the molecular branch, attention-based graph interaction is combined with multi-scale neighborhood aggregation to capture structural cues at different receptive fields. In the spectral branch, branch-specific attention enhancement and joint gating are introduced to better exploit the complementary characteristics of paired 1H and 13C spectra. The resulting molecular and spectral representations are integrated through a residual fusion module for final matching prediction. Experimental results on benchmark datasets demonstrate that SpecMol-MatchNet achieves consistently better overall performance than representative baseline methods.<\/jats:p>","DOI":"10.3390\/e28050532","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T14:05:46Z","timestamp":1778162746000},"page":"532","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multimodal Representation Learning Framework for Molecular Graph and NMR Spectrum Alignment"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5719-6939","authenticated-orcid":false,"given":"Xiao","family":"Li","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong-Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jiangxi Normal University, Nanchang 330022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5038-6541","authenticated-orcid":false,"given":"Jin-Biao","family":"Liu","sequence":"additional","affiliation":[{"name":"Jiangxi Provincial Key Laboratory of Functional Molecular Materials Chemistry, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5470-1203","authenticated-orcid":false,"given":"Xin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jiangxi Normal University, Nanchang 330022, China"},{"name":"Jiangxi Provincial Engineering Research Center of Blockchain Data Security and Governance, Nanchang 330031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22603","DOI":"10.1021\/acs.analchem.5c03783","article-title":"NMRMind: A Transformer-Based Model Enabling the Elucidation from Multidimensional NMR to Structures","volume":"97","author":"Xue","year":"2025","journal-title":"Anal. Chem."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1039\/a804433c","article-title":"Computer-assisted structure elucidation","volume":"16","author":"Jaspars","year":"1999","journal-title":"Nat. Prod. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1039\/C9NP00007K","article-title":"The role of computer-assisted structure elucidation (CASE) programs in the structure elucidation of complex natural products","volume":"36","author":"Burns","year":"2019","journal-title":"Nat. Prod. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alberts, M., Zipoli, F., and Vaucher, A. (2023, January 15). Learning the language of NMR: Structure elucidation from NMR spectra using transformer models. Proceedings of the AI for Accelerated Materials Design-NeurIPS 2023 Workshop, New Orleans, LA, USA.","DOI":"10.26434\/chemrxiv-2023-8wxcz"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2162","DOI":"10.1021\/acscentsci.4c01132","article-title":"Accurate and efficient structure elucidation from routine one-dimensional nmr spectra using multitask machine learning","volume":"10","author":"Hu","year":"2024","journal-title":"ACS Cent. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"015601","DOI":"10.1088\/2752-5724\/ae301f","article-title":"DiffNMR: Diffusion models for nuclear magnetic resonance spectra elucidation","volume":"5","author":"Yang","year":"2026","journal-title":"Mater. Futur."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1038\/s42256-022-00447-x","article-title":"Molecular contrastive learning of representations via graph neural networks","volume":"4","author":"Wang","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e17611","DOI":"10.1002\/ange.202517611","article-title":"Advancing Structure Elucidation with a Flexible Multi-Spectral AI Model","volume":"138","author":"Priessner","year":"2026","journal-title":"Angew. Chem."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1021\/acs.jctc.3c01256","article-title":"Highly accurate prediction of NMR chemical shifts from low-level quantum mechanics calculations using machine learning","volume":"20","author":"Li","year":"2024","journal-title":"J. Chem. Theory Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3746","DOI":"10.1021\/acs.jcim.0c00388","article-title":"General protocol for the accurate prediction of molecular 13C\/1H NMR chemical shifts via machine learning augmented DFT","volume":"60","author":"Gao","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wei, W., Liao, Y., Wang, Y., Wang, S., Du, W., Lu, H., Kong, B., Yang, H., and Zhang, Z. (2022). Deep learning-based method for compound identification in NMR spectra of mixtures. Molecules, 27.","DOI":"10.3390\/molecules27123653"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cort\u00e9s, I., Cuadrado, C., Hern\u00e1ndez Daranas, A., and Sarotti, A.M. (2023). Machine learning in computational NMR-aided structural elucidation. Front. Nat. Prod., 2.","DOI":"10.3389\/fntpr.2023.1122426"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103373","DOI":"10.1016\/j.drudis.2022.103373","article-title":"Deep learning methods for molecular representation and property prediction","volume":"27","author":"Li","year":"2022","journal-title":"Drug Discov. Today"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5624","DOI":"10.1021\/acs.jcim.4c00522","article-title":"Enhancing chemical reaction monitoring with a deep learning model for nmr spectra image matching to target compounds","volume":"64","author":"Tian","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100426","DOI":"10.1016\/j.caeai.2025.100426","article-title":"Artificial intelligence in multimodal learning analytics: A systematic literature review","volume":"8","author":"Mohammadi","year":"2025","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"bbaf629","DOI":"10.1093\/bib\/bbaf629","article-title":"ProtoMol: Enhancing molecular property prediction via prototype-guided multimodal learning","volume":"26","author":"Wang","year":"2025","journal-title":"Brief. Bioinform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3767728","article-title":"A comprehensive survey on multi-view classification: Methods, applications, and challenges","volume":"16","author":"Berahmand","year":"2025","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, H., Xu, J., Wen, G., Ren, Y., Zhao, N., and Zhu, X. (2025, January 16\u201322). Graph embedded contrastive learning for multi-view clustering. Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI\u201925, Montreal, ON, Canada.","DOI":"10.24963\/ijcai.2025\/594"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1801367","DOI":"10.1002\/advs.201801367","article-title":"Deep learning spectroscopy: Neural networks for molecular excitation spectra","volume":"6","author":"Ghosh","year":"2019","journal-title":"Adv. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.1039\/C9SC05704H","article-title":"Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy","volume":"11","author":"Schwaller","year":"2020","journal-title":"Chem. Sci."},{"key":"ref_21","unstructured":"Wang, Z., Jiang, T., Wang, J., and Xuan, Q. (2024). Multi-modal representation learning for molecular property prediction: Sequence, graph, geometry. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"16947","DOI":"10.1021\/acs.analchem.1c04307","article-title":"Cross-modal retrieval between 13C NMR spectra and structures for compound identification using deep contrastive learning","volume":"93","author":"Yang","year":"2021","journal-title":"Anal. Chem."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15329","DOI":"10.1039\/D1SC04105C","article-title":"A framework for automated structure elucidation from routine NMR spectra","volume":"12","author":"Huang","year":"2021","journal-title":"Chem. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1002\/mrc.5292","article-title":"Identifying molecular functional groups of organic compounds by deep learning of NMR data","volume":"60","author":"Li","year":"2022","journal-title":"Magn. Reson. Chem."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 22\u201325). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_26","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_27","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/5\/532\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T14:08:51Z","timestamp":1778162931000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/5\/532"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,7]]},"references-count":27,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["e28050532"],"URL":"https:\/\/doi.org\/10.3390\/e28050532","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,7]]}}}