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However, the low singlet oxygen quantum yield (\u03a6\n                    <jats:sub>\u0394<\/jats:sub>\n                    ) and structural optimization challenges hinder their further development. In this study, a machine learning (ML)\u2010assisted molecular design framework based on the currently available dataset of cyanine molecules is provided. By integrating RDKit structural descriptors with quantum chemical descriptors, hybrid feature\u2010based predictive models that accurately predict the \u03a6\n                    <jats:sub>\u0394<\/jats:sub>\n                    and fluorescence quantum yield (\u03a6\n                    <jats:sub>F<\/jats:sub>\n                    ) of cyanine derivatives (R\n                    <jats:sup>2<\/jats:sup>\n                    &gt;0.9) have been constructed for the first time. Based on this, a two\u2010stage virtual screening strategy is developed to efficiently identify promising cyanine derivatives from a library of 2835 candidate structures. Three representative molecules are synthesized as proof\u2010of\u2010concept validation, confirming the predictive reliability and practical utility of the ML\u2010guided workflow. The lead compound 1775 exhibits the highest performance (\u03a6\n                    <jats:sub>\u0394<\/jats:sub>\n                    = 0.62) and performs well in cellular assays, supporting the robustness and applicability of the ML\u2010assisted screening strategy in guiding experimental validation. This work establishes a data\u2010driven paradigm bridging molecular modeling and experimental verification, offering a reliable and generalizable approach for the rational design and rapid evaluation of high\u2010performance cyanine\u2010based theranostic agents.\n                  <\/jats:p>","DOI":"10.1002\/adma.202515813","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T06:52:09Z","timestamp":1763621529000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Machine Learning\u2010Assisted Design and Discovery of High\u2010Performance Cyanine\u2010Based Photosensitizers for Integrated Theranostic Applications"],"prefix":"10.1002","volume":"38","author":[{"given":"Bowen","family":"Diao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Fine Chemicals Frontiers Science Center for Smart Materials Dalian University of Technology  Dalian 116024 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