Rational Design of Organelle-Targeted Fluorescent Probes: Insights from Artificial Intelligence

Monitoring the physiological changes of organelles is essential for understanding the local biological information of cells, and for improving the diagnosis and therapy of diseases. Currently, fluorescent probes are considered as the most powerful tools for imaging and have been widely applied in biomedical fields. However, the expected targeting effects of these probes are often inconsistent with the real experiments. The design of fluorescent probes mainly depends on the empirical knowledge of researchers, which was inhibited by limited chemical space and low efficiency. Herein, we proposed a novel multi-level framework for the prediction of organelle-targeted fluorescent probes by employing advanced artificial intelligence algorithms. In this way, not only the targeting mechanism could be interpreted beyond intuitions but also a quick evaluation method could be established for the rational design. Furthermore, the targeting and imaging powers of the optimized and synthesized probes based on this methodology were verified by quantitative calculation and experiments.

Automated summary

Monitoring physiological changes of organelles is crucial for understanding cellular information and improving disease diagnosis and therapy. Fluorescent probes, considered as powerful imaging tools, have been widely used in biomedical fields. However, the design of these probes often lacks consistency with experimental results. To address this issue, we proposed a novel multi-level framework for predicting organelle-targeted fluorescent probes using advanced artificial intelligence algorithms. This approach not only provides a deeper understanding of the targeting mechanism but also enables quick evaluation and rational design. The optimized probes synthesized based on this methodology were verified for their targeting and imaging abilities through quantitative calculations and experiments.