{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T20:40:36Z","timestamp":1793047236010,"version":"4.5.0"},"reference-count":143,"publisher":"American Chemical Society (ACS)","license":[{"start":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T00:00:00Z","timestamp":1792972800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T00:00:00Z","timestamp":1792972800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T00:00:00Z","timestamp":1792972800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"name":"Yunnan Characteristic Plant Screening and Research and Development Service CXO Platform","award":["2022YKZY001"],"award-info":[{"award-number":["2022YKZY001"]}]},{"name":"Technology Planning Project from China Tobacco Yunnan Industrial Co., Ltd.","award":["2023JY01"],"award-info":[{"award-number":["2023JY01"]}],"id":[{"id":"https:\/\/ror.org\/030d08e08","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Science and Technology Program of Yunnan Universities for Serving Key Industries","award":["FWCY-BSPY2024004"],"award-info":[{"award-number":["FWCY-BSPY2024004"]}]},{"name":"Scientific Research Fund for Graduate Students of the Yunnan Provincial Department of Education","award":["2024Y025"],"award-info":[{"award-number":["2024Y025"]}],"id":[{"id":"https:\/\/ror.org\/02yrxdp92","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["82260835"],"award-info":[{"award-number":["82260835"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>1H and 13C NMR chemical-shift prediction has progressed from empirical rules and experimental databases to graph neural networks, three-dimensional molecular representations, and approaches that integrate quantum chemistry with machine learning; its applications have likewise expanded from chemical-shift calculation for known structures to experimental spectral assignment, candidate-structure discrimination, and automated structure elucidation. Forward structure-to-NMR prediction can generate predicted spectral-library data for candidate molecules and support structure retrieval and candidate screening by matching predicted and experimental chemical shifts; inverse NMR-to-structure analysis progressively constrains the candidate space using structural constraints encoded in experimental spectra. This Review systematically surveys the development and growing integration of these two directions, focusing on how chemical-shift prediction contributes to peak-to-atom assignment, candidate-structure comparison, and automated structure elucidation, and examining how prediction error, candidate coverage, and experimental data quality affect final structural decisions. More recently, agentic approaches have begun to organize existing NMR prediction and structure-elucidation tools into iterative analytical systems that continuously update candidate structures from current spectroscopic evidence and select subsequent computational or experimental operations; further progress in automated structure elucidation depends on reliable predicted spectral libraries, clearly defined model applicability domains, and structural conclusions that can be tested against experimental spectroscopic evidence.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c02915","type":"journal-article","created":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T20:01:49Z","timestamp":1793044909000},"source":"Crossref","is-referenced-by-count":0,"title":["Small-Molecule 1H and 13C NMR:\nFrom Chemical-Shift Prediction to Automated Structure Elucidation"],"prefix":"10.1021","author":[{"given":"Tingcai","family":"Dong","sequence":"first","affiliation":[{"name":"Yunnan University , , ,","place":["Kunming, China, 650091"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanling","family":"Deng","sequence":"additional","affiliation":[{"name":"Yunnan University , , ,","place":["Kunming, China, 650091"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqiu","family":"Tai","sequence":"additional","affiliation":[{"name":"The University of Melbourne , , , ,","place":["Melbourne, Victoria, Australia, 3010"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Yunnan University , , ,","place":["Kunming, China, 650091"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongfan","family":"Zi","sequence":"additional","affiliation":[{"name":"Yunnan University , , ,","place":["Kunming, China, 650091"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhu","sequence":"additional","affiliation":[{"name":"Yunnan University , , ,","place":["Kunming, China, 650091"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengmei","family":"Zhang","sequence":"additional","affiliation":[{"name":"R&D Center of China Tobacco Yunnan Industrial Co., Ltd. , ,","place":["Kunming, People\u2019s Republic of China, 650231"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0970-4920","authenticated-orcid":true,"given":"Le","family":"Cai","sequence":"additional","affiliation":[{"name":"Yunnan University , , ,","place":["Kunming, China, 650091"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,10,26]]},"reference":[{"key":"2026102616014335000_cit1","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1002\/mrc.5234","article-title":"Prediction of chemical shift in NMR:\na review","volume":"60","author":"Jonas","year":"2022","journal-title":"Magn. 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