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Artificial Intelligence-Guided Design of Fluorescent Probes for Biomedical Applications.

Pan Tao1,2, Pengzhan Wang1,2, Dushuo Feng1

  • 1Frontiers Science Center For Transformative Molecules, School of Chemistry and Chemical Engineering, School of Biomedical Engineering, National Center for Translational Medicine, Zhangjiang Institute for Advanced Study, National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, China.

Chemistry (Weinheim an Der Bergstrasse, Germany)
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Artificial intelligence (AI) accelerates the design of fluorescent probes for bioimaging. AI optimizes probe properties and enables new applications in imaging, sensing, and therapy, overcoming traditional limitations.

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artificial intelligencebiomedical applicationsfluorescent probesstructure‐property‐function relationships

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Area of Science:

  • Biomedical Engineering
  • Chemical Biology
  • Artificial Intelligence

Background:

  • Fluorescence imaging (FLI) is crucial in biomedical research due to its high sensitivity and resolution.
  • Fluorescent probe performance dictates FLI effectiveness but is limited by empirical design methods.
  • Artificial intelligence (AI) offers a novel approach for rational probe design and optimization.

Purpose of the Study:

  • To provide a comprehensive review of AI-guided design strategies for fluorescent probes in bioimaging.
  • To elucidate the workflow of AI-based predictive frameworks for probe development.
  • To discuss the impact of AI on optimizing probe properties and their biomedical applications.

Main Methods:

  • Review of recent literature on AI applications in fluorescent probe design.
  • Analysis of AI's role in predicting and optimizing photophysical, targeting, and responsive probe characteristics.
  • Examination of AI-guided probes in various biomedical applications, including imaging, sensing, and therapy.

Main Results:

  • AI enables rapid property prediction, high-throughput screening, and inverse design of fluorescent probes.
  • AI significantly enhances optical performance, targeting specificity, and responsiveness of probes.
  • AI-guided probes show promise in advanced bioimaging, diagnostics, and therapeutic interventions.

Conclusions:

  • AI revolutionizes fluorescent probe design, moving beyond traditional trial-and-error methods.
  • AI facilitates the development of sophisticated probes for diverse biomedical applications.
  • Addressing current challenges in AI-guided probe design will accelerate future advancements in bioimaging.