{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T13:08:36Z","timestamp":1788872916598,"version":"build-2803163510"},"reference-count":58,"publisher":"American Chemical Society (ACS)","issue":"35","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["JYB2025XDXM109"],"award-info":[{"award-number":["JYB2025XDXM109"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JZ2024HGTB0231"],"award-info":[{"award-number":["JZ2024HGTB0231"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22204038"],"award-info":[{"award-number":["22204038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>NMR spectroscopy has become an indispensable tool in chemistry and life sciences, yet its high resolution is generally constrained by the cost of increased acquisition times. Recently, sparse data sampling combined by deep learning (DL) reconstruction serves as an effective manner to highly speed up NMR acquisition while maintaining satisfactory spectral quality. However, existing end-to-end DL models are generally designed specifically for a single spectroscopy type and operate as \u201cblack boxes\u201d, hindering their generalizable applications and trustworthy interpretation in practical deployments. Here, we introduce a unified deep reconstruction network architecture for multitask compressed sensing NMR spectroscopy by unrolling the classical iterative shrinkage-thresholding algorithm. First, this framework fully exploits the theoretical foundations and multitask adaptability of a compressed sensing solver to break the task-customized network design and enable a single network to be simultaneously applied to two NMR sparse reconstruction tasks, in which a common network architecture is trained separately for different applications. Second, benefiting from leveraging both model-driven reliability and data-driven expressivity, the proposed model provides reconstruction performance, outperforming specialized state-of-the-art models tailored to individual tasks, and implements improved transparency of the reconstruction process via stage-wise visualization. Therefore, our findings somewhat respond to critical concerns of adaptability and interpretability inherent in DL models and support more reliable DL-assisted spectroscopic interpretation, thus boosting the potential of artificial intelligence to extend the scope of NMR methodologies.<\/jats:p>","DOI":"10.1021\/acs.analchem.6c01352","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T14:40:56Z","timestamp":1784212856000},"page":"25597-25606","source":"Crossref","is-referenced-by-count":0,"title":["UNMR-Net: A\nUnified Deep Reconstruction Network for\nMultitask Compressed Sensing NMR Spectroscopy"],"prefix":"10.1021","volume":"98","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8202-5639","authenticated-orcid":true,"given":"Haolin","family":"Zhan","sequence":"first","affiliation":[{"name":"Hefei University of Technology , , ,","place":["Hefei, China, 230009"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixin","family":"Ye","sequence":"additional","affiliation":[{"name":"Hefei University of Technology , , ,","place":["Hefei, China, 230009"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaojie","family":"Xing","sequence":"additional","affiliation":[{"name":"Hefei University of Technology , , ,","place":["Hefei, China, 230009"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Hefei University of Technology , , ,","place":["Hefei, China, 230009"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifei","family":"Ji","sequence":"additional","affiliation":[{"name":"Xiamen University , , ,","place":["Xiamen, China, 361005"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"issue":"6694","key":"2026090808075849100_cit1","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1126\/science.adk1687","article-title":"Expansive discovery of chemically diverse structured\nmacrocyclic oligoamides","volume":"384","author":"Salveson","year":"2024","journal-title":"Science"},{"issue":"8009","key":"2026090808075849100_cit2","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1038\/s41586-024-07210-9","article-title":"Dearomatization\ndrives complexity generation in freshwater organic matter","volume":"628","author":"Li","year":"2024","journal-title":"Nature"},{"issue":"8","key":"2026090808075849100_cit3","doi-asserted-by":"publisher","first-page":"5063","DOI":"10.1021\/jacs.3c14359","article-title":"Measuring protein\u2013ligand\nbinding by hyperpolarized ultrafast NMR","volume":"146","author":"Qi","year":"2024","journal-title":"J.\nAm. 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Rev."},{"key":"2026090808075849100_cit7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.pnmrs.2015.02.002","article-title":"Pure shift\nNMR","volume":"86\u201387","author":"Zangger","year":"2015","journal-title":"Prog. Nucl. Magn. Reson. Spectrosc."},{"issue":"53","key":"2026090808075849100_cit8","doi-asserted-by":"publisher","first-page":"13983","DOI":"10.1002\/chem.201800524","article-title":"PSYCHE pure\nshift NMR spectroscopy","volume":"24","author":"Foroozandeh","year":"2018","journal-title":"Chem. Eur. J."},{"issue":"23","key":"2026090808075849100_cit9","doi-asserted-by":"publisher","first-page":"7356","DOI":"10.1021\/acs.jpclett.9b03092","article-title":"High-resolution\nprobing of heterogeneous\nsamples by spatially selective pure shift NMR spectroscopy","volume":"10","author":"Zhan","year":"2019","journal-title":"J. Phys. Chem. 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Imag."},{"issue":"45","key":"2026090808075849100_cit31","doi-asserted-by":"publisher","first-page":"16567","DOI":"10.1021\/acs.analchem.3c02688","article-title":"Restore\nhigh-resolution nuclear magnetic resonance spectra from inhomogeneous\nmagnetic fields using a neural network","volume":"95","author":"Xiao","year":"2023","journal-title":"Anal.\nChem."},{"issue":"26","key":"2026090808075849100_cit32","doi-asserted-by":"publisher","first-page":"10297","DOI":"10.1002\/anie.201908162","article-title":"Accelerated nuclear\nmagnetic resonance spectroscopy with deep learning","volume":"59","author":"Qu","year":"2020","journal-title":"Angew. Chem., Int. 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Chem."},{"issue":"15","key":"2026090808075849100_cit36","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1002\/cphc.201700474","article-title":"Enabling fast pseudo-2D NMR spectral acquisition for\nbroadband homonuclear decoupling: the EXACT NMR approach","volume":"18","author":"Ndukwe","year":"2017","journal-title":"ChemPhyschem"},{"issue":"64","key":"2026090808075849100_cit37","doi-asserted-by":"publisher","first-page":"9563","DOI":"10.1039\/C9CC05222D","article-title":"Accelerated acquisition in pure-shift\nspectra based on prior knowledge from 1H NMR","volume":"55","author":"Shchukina","year":"2019","journal-title":"Chem. Commun."},{"key":"2026090808075849100_cit38","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1038\/s42004-026-01912-z","article-title":"Physics-informed deep learning enables\nfast ultrahigh-resolution nuclear magnetic resonance spectroscopy","volume":"9","author":"Bao","year":"2026","journal-title":"Commun. Chem."},{"issue":"29","key":"2026090808075849100_cit39","doi-asserted-by":"publisher","first-page":"2309810","DOI":"10.1002\/advs.202309810","article-title":"Fast pure shift\nNMR spectroscopy using attention-assisted\ndeep neural network","volume":"11","author":"Zhan","year":"2024","journal-title":"Adv. Sci."},{"issue":"4","key":"2026090808075849100_cit40","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1021\/acs.analchem.3c04007","article-title":"Accelerated\npure shift NMR spectroscopy with deep learning","volume":"96","author":"Zhan","year":"2024","journal-title":"Anal. Chem."},{"issue":"14","key":"2026090808075849100_cit41","doi-asserted-by":"publisher","first-page":"3397","DOI":"10.1021\/acs.jpclett.3c00455","article-title":"Deep learning\nmethodology for obtaining\nultraclean pure shift proton nuclear magnetic resonance spectra","volume":"14","author":"Yang","year":"2023","journal-title":"J. Phys. Chem. Lett."},{"issue":"33","key":"2026090808075849100_cit42","doi-asserted-by":"publisher","first-page":"13742","DOI":"10.1021\/acs.analchem.4c03146","article-title":"Combining fast pure shift NMR and\nGEMSTONE-based selective TOCSY for efficient NMR analysis of complex\nsystems","volume":"96","author":"Zhan","year":"2024","journal-title":"Anal. Chem."},{"issue":"10","key":"2026090808075849100_cit43","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1002\/mrc.4705","article-title":"Compressed\nNMR: Combining compressive sampling and\npure shift NMR techniques","volume":"56","author":"Aguilar","year":"2018","journal-title":"Magn. Reson. Chem."},{"issue":"2","key":"2026090808075849100_cit44","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s10858-016-0072-7","article-title":"Sparse multidimensional\niterative lineshape-enhanced (SMILE) reconstruction of both non-uniformly\nsampled and conventional NMR data","volume":"68","author":"Ying","year":"2017","journal-title":"J. Biomol.\nNMR"},{"issue":"24","key":"2026090808075849100_cit45","doi-asserted-by":"publisher","first-page":"5556","DOI":"10.1002\/anie.201100370","article-title":"Accelerated NMR spectroscopy by using\ncompressed sensing","volume":"50","author":"Kazimierczuk","year":"2011","journal-title":"Angew. Chem., Int. Ed."},{"issue":"29","key":"2026090808075849100_cit46","doi-asserted-by":"publisher","first-page":"6548","DOI":"10.1002\/anie.201100440","article-title":"Fast multidimensional\nNMR spectroscopy using compressed sensing","volume":"50","author":"Holland","year":"2011","journal-title":"Angew.\nChem., Int. Ed."},{"issue":"44","key":"2026090808075849100_cit47","doi-asserted-by":"publisher","first-page":"13895","DOI":"10.1021\/bi961186a","article-title":"Interaction of soluble\ncellooligosaccharides with the N-terminal cellulose-binding domain\nof Cellulomonas fimi CenC 2. NMR and ultraviolet absorption spectroscopy","volume":"35","author":"Johnson","year":"1996","journal-title":"Biochemistry"},{"issue":"41","key":"2026090808075849100_cit48","doi-asserted-by":"publisher","first-page":"16935","DOI":"10.1021\/jacs.1c04010","article-title":"Virtual homonuclear decoupling in\ndirect detection nuclear magnetic resonance experiments using deep\nneural networks","volume":"143","author":"Karunanithy","year":"2021","journal-title":"J. Am. Chem. Soc."},{"key":"2026090808075849100_cit49","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/S1872-2067(23)64526-7","article-title":"In situ monitoring\nmulti-carbon alcohol oxidation by combined electrochemistry with spatially\nselective NMR spectroscopy","volume":"53","author":"Zhan","year":"2023","journal-title":"Chin. J. Catal."},{"key":"2026090808075849100_cit50","doi-asserted-by":"publisher","first-page":"106772","DOI":"10.1016\/j.jmr.2020.106772","article-title":"Fast reconstruction\nof non-uniform sampling multidimensional NMR spectroscopy via a deep\nneural network","volume":"317","author":"Luo","year":"2020","journal-title":"J. Magn. Reson"},{"issue":"6","key":"2026090808075849100_cit51","doi-asserted-by":"publisher","first-page":"2699","DOI":"10.1021\/acs.analchem.1c03883","article-title":"Neural network\nmethod for diffusion-ordered NMR spectroscopy","volume":"94","author":"Lin","year":"2022","journal-title":"Anal. Chem."},{"issue":"51","key":"2026090808075849100_cit52","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adr2155","article-title":"A combined NMR and\ndeep neural network approach for enhancing the spectral resolution\nof aromatic side chains in proteins","volume":"10","author":"Shukla","year":"2024","journal-title":"Sci. Adv."},{"issue":"1","key":"2026090808075849100_cit53","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1038\/s42004-024-01325-w","article-title":"Beyond traditional\nmagnetic resonance processing with\nartificial intelligence","volume":"7","author":"Jahangiri","year":"2024","journal-title":"Commun. Chem."},{"issue":"1","key":"2026090808075849100_cit54","doi-asserted-by":"publisher","first-page":"2342","DOI":"10.1038\/s41467-025-57721-w","article-title":"Deep learning network\nfor NMR spectra reconstruction in time-frequency domain and quality\nassessment","volume":"16","author":"Luo","year":"2025","journal-title":"Nat. Commun."},{"key":"2026090808075849100_cit55","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adw1379","article-title":"High-confidence reconstruction\nfor Laplace\ninversion in NMR based on uncertainty- informed deep learning","volume":"11","author":"Chen","year":"2025","journal-title":"Sci. Adv."},{"issue":"17","key":"2026090808075849100_cit56","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2527937123","article-title":"Two-dimensional NMR from a single\npulse: Reconstructing heteronuclear 2D spectra via off-resonance decoupling\nand deep neural networks","volume":"123","author":"Khandave","year":"2026","journal-title":"Proc. Natl. Acad.\nSci. U. S. A."},{"key":"2026090808075849100_cit57","doi-asserted-by":"publisher","first-page":"9226","DOI":"10.1021\/jacs.5c22860","article-title":"Deep learning assisted proton pure shift NMR spectroscopy","volume":"148","author":"Kakita","year":"2026","journal-title":"J. Am. Chem. Soc."},{"key":"2026090808075849100_cit58","doi-asserted-by":"publisher","first-page":"12362","DOI":"10.1021\/acs.analchem.5c07201","article-title":"Uncertainty exploration of deep learning enabled fast multidimensional\nNMR spectroscopy of proteins","volume":"98","author":"Zhan","year":"2026","journal-title":"Anal. Chem."}],"container-title":["Analytical Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.analchem.6c01352","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/ancham\/article-pdf\/98\/35\/25597\/65979740\/acs.analchem.6c01352.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/ancham\/article-pdf\/98\/35\/25597\/65979740\/acs.analchem.6c01352.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T12:30:31Z","timestamp":1788870631000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/ancham\/article\/98\/35\/25597\/5212176\/UNMR-Net-A-Unified-Deep-Reconstruction-Network-for"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"references-count":58,"journal-issue":{"issue":"35","published-online":{"date-parts":[[2026,7,16]]},"published-print":{"date-parts":[[2026,9,8]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.analchem.6c01352","relation":{},"ISSN":["0003-2700","1520-6882"],"issn-type":[{"value":"0003-2700","type":"print"},{"value":"1520-6882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]}}}