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Enhancing fluorescent probe design through multilayer interaction convolutional networks: advancing biosensing and

Gongcheng Ma1, Qihang Ding2, Yuding Zhang3

  • 1School of Life Science and Technology, Xinxiang Medical University Xinxiang 453003 China.

Chemical Science
|April 24, 2025
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Summary

Researchers developed a machine learning model to predict fluorescent probe spectra, significantly speeding up the design of custom rhodamine probes for bioimaging and biosensing applications.

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Area of Science:

  • Bioanalytical Chemistry
  • Molecular Imaging
  • Chemical Biology

Background:

  • Fluorescent probes are essential tools in biosensing and bioimaging.
  • Current probe design is empirical, time-consuming, and labor-intensive.
  • Precise spectral tailoring is crucial for high-performance fluorescent probes.

Purpose of the Study:

  • To develop a computational method for predicting excitation and emission wavelengths of rhodamine fluorescent probes.
  • To accelerate the design process for spectrally customized fluorescent probes.
  • To improve the accuracy and reliability of fluorescent probe development.

Main Methods:

  • Compiled a dataset of over 600 rhodamine fluorescent probes.
  • Utilized a multilayer interaction convolutional model (MICNet) trained on molecular fingerprints.
  • Implemented a closed-loop strategy with experimental feedback for iterative algorithm enhancement.

Main Results:

  • MICNet achieved high accuracy in predicting wavelengths: 0.1% MRE for excitation and 0.4% MRE for emission.
  • The closed-loop strategy iteratively improved the design algorithm's accuracy.
  • Demonstrated accelerated development of spectrally customized fluorescent probes.

Conclusions:

  • The developed computational approach significantly streamlines fluorescent probe design.
  • This method accelerates the creation of custom fluorescent probes for bioanalytical applications.
  • Offers a substantial advancement in the field of bioanalytical chemistry and molecular imaging.