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Chemicals are one of the most searched biomedical entities in PubMed, and\u2014as highlighted during the coronavirus disease 2019 pandemic\u2014their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e.\u00a0named entity recognition (NER)] and normalization (i.e.\u00a0entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672\u2009F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136\u2009F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073\u2009F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substantially more challenging. We look forward to further developing biomedical text\u2013mining methods to respond to the rapid growth of biomedical literature. The NLM-Chem track dataset and other challenge materials are publicly available at https:\/\/ftp.ncbi.nlm.nih.gov\/pub\/lu\/BC7-NLM-Chem-track\/.<\/jats:p><jats:p>Database URL https:\/\/ftp.ncbi.nlm.nih.gov\/pub\/lu\/BC7-NLM-Chem-track\/<\/jats:p>","DOI":"10.1093\/database\/baad005","type":"journal-article","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:18:28Z","timestamp":1678234708000},"source":"Crossref","is-referenced-by-count":9,"title":["Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII"],"prefix":"10.1093","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3296-5766","authenticated-orcid":false,"given":"Robert","family":"Leaman","sequence":"first","affiliation":[{"name":"National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health , 8600 Rockville Pike, Bethesda, MD 20894, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5651-1860","authenticated-orcid":false,"given":"Rezarta","family":"Islamaj","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health , 8600 Rockville Pike, Bethesda, MD 20894, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Virginia","family":"Adams","sequence":"additional","affiliation":[{"name":"NVIDIA , 2788 San Tomas Expressway, Santa Clara, CA 95051, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed A","family":"Alliheedi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Al Baha University , 4781 King Fahd Rd, Al Aqiq 65779, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jo\u00e3o Rafael","family":"Almeida","sequence":"additional","affiliation":[{"name":"Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro , Campus Universit\u00e1rio de Santiago, Aveiro 3810-193, Portugal"},{"name":"Department of Information and Communications Technologies, University of A Coru\u00f1a , Cami\u00f1o do Lagar de Castro, A Coru\u00f1a 15008, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3533-8872","authenticated-orcid":false,"given":"Rui","family":"Antunes","sequence":"additional","affiliation":[{"name":"Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro , Campus Universit\u00e1rio de Santiago, Aveiro 3810-193, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Bevan","sequence":"additional","affiliation":[{"name":"Informatics Department, Medicines Discovery Catapult , Alderley Park, Block 35, Mereside, Macclesfield SK10 4ZF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9634-8380","authenticated-orcid":false,"given":"Yung-Chun","family":"Chang","sequence":"additional","affiliation":[{"name":"Graduate Institute of Data Science, Taipei Medical University , No. 172-1, Section 2, Keelung Rd, Da\u2019an District, Taipei City , Taipei 106, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arslan","family":"Erdengasileng","sequence":"additional","affiliation":[{"name":"Department of Statistics, Florida State University , 117 N. 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