高性能荧光探针是荧光引导成像中不可或缺的工具，对于精确描绘病灶组织同时最大限度地减少不必要的健康组织切除至关重要。在此，我们首先提出了机器学习辅助策略来研究当前可用的呫吨染料，并构建了定量预测模型来指导具有所需pH响应性的新型荧光分子的合理合成。我们成功获得了两种新型Si-罗丹明衍生物，并构建了组织蛋白酶/pH顺序激活探针SiR-CTS-pH。结果表明，与单一 pH 或组织蛋白酶激活探针相比，SiR-CTS-pH 具有更高的荧光成像信噪比。此外，SiR-CTS-pH对肿瘤细胞和组织表现出很强的分化能力，能够准确区分复杂的肝细胞癌组织和正常组织，显示出其在临床实践中的巨大应用潜力。因此，呫吨染料的不断开发以及通过机器学习辅助模型合理设计优异的荧光分子将为研究人员拓宽道路并提供更先进的方法。本文受版权保护。保留所有权利。本文受版权保护。版权所有。

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.This article is protected by copyright. All rights reserved.