ABSTRACT Fluorescence imaging (FLI) has become a cornerstone for biomedical research owing to its non‐invasive, high sensitivity, and exceptional spatial resolution. The effectiveness of FLI is primarily governed by the properties of fluorescent probes, including photophysical characteristics, targeting capabilities, and responsiveness. However, current probe development in FLI relies on empirical trial‐and‐error strategies or quantum‐chemical calculations, both of which are time‐consuming, labor‐intensive, and often insufficient for precisely deciphering complex structure‐property‐function relationships. Recent advances in artificial intelligence (AI) have opened a new avenue for rational probe design through rapid property prediction, high‐throughput molecular screening, and performance‐guided inverse design. Nevertheless, a systematic and comprehensive review elucidating AI‐guided design of fluorescent probes in biomedical applications is still lacking. This review focuses on recent advances in AI‐guided design and performance optimization of fluorescent probes for bioimaging applications. First, the conceptual workflow underlying AI‐based predictive frameworks is elucidated. Second, the impact of AI on optimizing key probe properties—including optical performance, targeting, and responsive capabilities—is thoroughly elaborated. Third, the biomedical applications of AI‐guided probes in imaging, sensing, and therapy are reviewed. Finally, the current challenges and future perspectives to accelerate the development of AI‐guided fluorescent probe design are briefly discussed.
Tao et al. (Fri,) studied this question.