Machine Learning-assisted Rational Design of Si-rhodamine as Cathepsin-pH Activated Probe for Accurate Fluorescence Navigation.

High-performance fluorescent probes stand as indispensable tools in fluorescence-guided imaging, and are crucial for precise delineation of focal tissue while minimizing unnecessary removal of healthy tissue. Herein, we firstly proposed machine learning-assisted strategy to investigate the current available xanthene dyes, and constructed a quantitative prediction model to guide the rational synthesis of novel fluorescent molecules with desired pH responsivity. We successfully achieved two novel Si-rhodamine derivatives and constructed the Cathepsin/pH sequentially activated probe SiR-CTS-pH. The results reveal that SiR-CTS-pH exhibits higher signal-to-noise ratio of fluorescence imaging, compared to single pH or cathepsin-activate probe. Moreover, SiR-CTS-pH shows strong differentiation abilities for tumor cells and tissues and accurately discriminates the complex hepatocellular carcinoma tissues from normal ones, indicating its significant application potential in clinical practice. Therefore, the continuous development of xanthene dyes and the rational design of superior fluorescent molecules through Machine Learning-assisted model will broaden the path and provide more advanced methods to researchers. This article is protected by copyright. All rights reserved.