{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T12:50:14Z","timestamp":1792068614432,"version":"4.4.0"},"reference-count":33,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82530069"],"award-info":[{"award-number":["82530069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82561160164"],"award-info":[{"award-number":["82561160164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Small"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Fluorescence imaging in the second near\u2010infrared window (NIR\u2010II, 1000\u20131700\u00a0nm) has attracted increasing attention in biomedical applications owing to its deep tissue penetration, high spatial resolution, and low background autofluorescence. However, the rational optimization of high\u2010performance NIR\u2010II fluorophores remains challenging because of the complex relationship between molecular structure and optical properties. In this study, we integrated existing fluorescence datasets with NIR\u2010II fluorophore data to construct a comprehensive dataset covering maximum emission wavelengths from the visible region to the NIR\u2010II window. Based on this dataset, we developed a large language model (LLM)\u2010based framework for predicting molecular maximum emission wavelengths and compared its performance with conventional machine learning and deep learning models. The results demonstrated that the LLM achieved competitive predictive performance compared with the baseline models, highlighting the feasibility of LLM\u2010based structure\u2013property prediction for fluorescent molecules. Model\u2010guided virtual screening was subsequently performed to prioritize candidate NIR\u2010II fluorophores, leading to the identification of two promising candidates that were experimentally characterized and exhibited favorable imaging performance in vitro and in vivo. Overall, this study demonstrates the feasibility of LLM\u2010assisted structure\u2013property prediction and virtual screening for NIR\u2010II fluorophores, providing an efficient computational strategy for candidate prioritization and molecular optimization.<\/jats:p>","DOI":"10.1002\/smll.76251","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T12:13:12Z","timestamp":1792066392000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large Language Model\u2010Assisted Prediction and Virtual Screening of Near\u2010Infrared\u2010II Fluorescent Molecules"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0434-8747","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Drug Research Molecular Imaging Center Shanghai Institute of Materia Medica Chinese Academy of Sciences  Shanghai 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