Machine Learning‐Assisted Design and Discovery of High‐Performance Cyanine‐Based Photosensitizers for Integrated Theranostic Applications

Abstract Cyanine‐based photosensitizers are attractive for photodynamic therapy (PDT) owing to their excellent near‐infrared absorption and fluorescence. However, the low singlet oxygen quantum yield (Φ Δ ) and structural optimization challenges hinder their further development. In this study, a machine learning (ML)‐assisted 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‐based predictive models that accurately predict the Φ Δ and fluorescence quantum yield (Φ F ) of cyanine derivatives (R 2 >0.9) have been constructed for the first time. Based on this, a two‐stage 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‐of‐concept validation, confirming the predictive reliability and practical utility of the ML‐guided workflow. The lead compound 1775 exhibits the highest performance (Φ Δ = 0.62) and performs well in cellular assays, supporting the robustness and applicability of the ML‐assisted screening strategy in guiding experimental validation. This work establishes a data‐driven paradigm bridging molecular modeling and experimental verification, offering a reliable and generalizable approach for the rational design and rapid evaluation of high‐performance cyanine‐based theranostic agents.