{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:06:14Z","timestamp":1779253574094,"version":"3.51.4"},"reference-count":17,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:00:00Z","timestamp":1778371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Infrastructure Platform Project","award":["APT2401-5"],"award-info":[{"award-number":["APT2401-5"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>This study presents TCM-MS2Link, a standardized mass spectrometry-based association dataset for traditional Chinese medicine (TCM), serving as an important resource for natural product research in TCM. The dataset adopts a dual-layer \u201cknowledge\u2013data\u201d architecture: the first layer, TCM-MolLink, comprises curated herb\u2013compound association data, constructed through the integration of multiple heterogeneous databases and rigorous consistency filtering to establish high-confidence relationships between TCM herbs and their chemical constituents; the second layer, MS2-MLReady, is a benchmark dataset for mass spectrometry-based machine learning which, after systematic data cleaning, standardized preprocessing, and well-designed data partitioning, can directly support the training and evaluation of artificial intelligence models. By addressing key limitations in existing public resources, including data fragmentation, inconsistent annotations, and insufficient computational usability, TCM-MS2Link effectively overcomes major bottlenecks in the systematic analysis of TCM components and data-driven research. This study significantly enhances the reliability of herb\u2013compound associations and the modeling readiness of mass spectrometry data, providing a high-quality, standardized, and reusable data foundation for applications such as TCM knowledge base construction and automated spectrum\u2013structure identification, thereby promoting the advancement of TCM informatics and data-driven research.<\/jats:p>","DOI":"10.3390\/data11050113","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T10:50:52Z","timestamp":1778496652000},"page":"113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TCM-MS2Link: A Unified AI-Ready Dataset Integrating TCM Herb\u2013Compound Knowledge and MS\/MS Spectral Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Qianjin","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feifan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5059-102X","authenticated-orcid":false,"given":"Lin","family":"Guo","sequence":"additional","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4629-8626","authenticated-orcid":false,"given":"Xingchuang","family":"Xiong","sequence":"additional","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1021\/acs.jnatprod.9b01285","article-title":"Natural Products as Sources of New Drugs over the Nearly Four Decades from 01\/1981 to 09\/2019","volume":"83","author":"Newman","year":"2020","journal-title":"J. Nat. Prod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"307","DOI":"10.2165\/00126839-200809050-00002","article-title":"Applications of Metabolomics in Drug Discovery and Development","volume":"9","author":"Wishart","year":"2008","journal-title":"Drugs R & D"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhao, F., Li, Q., Wang, M., and Xiong, X. (2025). An AI Agent-Based System for Retrieving Compound Information in Traditional Chinese Medicine. Information, 16.","DOI":"10.3390\/info16070543"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11306-014-0676-4","article-title":"Competitive Fragmentation Modeling of ESI-MS\/MS Spectra for Putative Metabolite Identification","volume":"11","author":"Allen","year":"2015","journal-title":"Metabolomics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1093\/bioinformatics\/btz736","article-title":"Identification of Metabolites from Tandem Mass Spectra with a Machine Learning Approach Utilizing Structural Features","volume":"36","author":"Li","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_6","unstructured":"(2026, January 07). ETCM: The Encyclopedia of Traditional Chinese Medicine. Available online: http:\/\/www.tcmip.cn\/ETCM\/."},{"key":"ref_7","unstructured":"(2026, January 07). TCMSP: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. Available online: https:\/\/www.tcmsp-e.com\/load_intro.php."},{"key":"ref_8","unstructured":"(2026, January 07). HERB: A High-Throughput Experiment- and Reference-Guided Database of Traditional Chinese Medicine. Available online: http:\/\/herb.ac.cn\/."},{"key":"ref_9","unstructured":"(2026, January 07). PubChem, Available online: https:\/\/pubchem.ncbi.nlm.nih.gov\/."},{"key":"ref_10","unstructured":"(2026, January 07). GNPS: Global Natural Products Social Molecular Networking. Available online: https:\/\/ccms-ucsd.github.io\/GNPSDocumentation\/gnpslibraries."},{"key":"ref_11","unstructured":"(2026, January 07). MassBank: Public Mass Spectral Database. Available online: https:\/\/github.com\/MassBank\/MassBank-data\/releases."},{"key":"ref_12","unstructured":"(2026, January 07). MoNA: MassBank of North America. Available online: https:\/\/mona.fiehnlab.ucdavis.edu\/downloads."},{"key":"ref_13","unstructured":"(2026, January 07). Zenodo. Available online: https:\/\/zenodo.org\/records\/11163381."},{"key":"ref_14","unstructured":"Bittremieux, W., B\u00f6cker, S., Brungs, C., Bushuiev, A., Bushuiev, R., de Jonge, N., D\u00fchrkop, K., Greiner, R., Hassoun, S., and Haupt, N. (2026). MassSpecGym: A Benchmark for the Discovery and Identification of Molecules, Neural Information Processing Systems Foundation, Inc. (NeurIPS)."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1038\/s41592-019-0344-8","article-title":"SIRIUS 4: A Rapid Tool for Turning Tandem Mass Spectra into Metabolite Structure Information","volume":"16","author":"Fleischauer","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1038\/s41587-025-02663-3","article-title":"Self-Supervised Learning of Molecular Representations from Millions of Tandem Mass Spectra Using DreaMS","volume":"44","author":"Bushuiev","year":"2026","journal-title":"Nat. Biotechnol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0169-409X(96)00423-1","article-title":"Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings","volume":"23","author":"Lipinski","year":"1997","journal-title":"Adv. Drug Deliv. 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