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Related Experiment Video

Updated: Jun 21, 2026

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy
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Tree-Based Machine Learning Model for Fluorescence Lifetime Prediction in Organic Compounds.

Dragoș-Cătălin Vovea1, Vasile Chiș2

  • 1Faculty of Physics, Babeș-Bolyai University, Str. M. Kogălniceanu 1, Cluj-Napoca, RO- 400084, Romania.

Journal of Fluorescence
|June 19, 2026
PubMed
Summary
This summary is machine-generated.

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A new machine learning model accurately predicts organic chromophore fluorescence lifetimes. This tool aids in fluorescence imaging and materials screening by analyzing molecular structure and solvent effects.

Area of Science:

  • Photophysical Chemistry
  • Computational Chemistry
  • Machine Learning

Background:

  • Fluorescence lifetime is crucial for understanding excited-state decay dynamics.
  • It is essential for applications like fluorescence imaging, sensing, optoelectronics, and materials screening.
  • Predicting fluorescence lifetimes computationally can accelerate materials discovery.

Purpose of the Study:

  • Develop a machine learning framework to predict fluorescence lifetimes.
  • Utilize diverse molecular and environmental descriptors for accurate predictions.
  • Benchmark regression algorithms to identify the optimal model.

Main Methods:

  • Employed a tree-based machine learning framework, selecting LightGBM as the final model.
  • Encoded molecular structures using RDKit descriptors, fingerprints, solvent descriptors, and interaction features.
Keywords:
ChromophoresFluorescence lifetime predictionLightGBMMolecular descriptorsSHAP analysisTree-based machine learning

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  • Validated the model using cluster-stratified 10-fold cross-validation and external datasets.

    • Main Results:

    • Achieved a mean absolute error (MAE) of 0.8324 ± 0.0617 ns and R² of 0.7523 ± 0.0317.
    • The model demonstrated reliable performance in the intermediate lifetime range, with larger deviations for extreme lifetimes.
    • External validation confirmed the model's ability to capture global trends and serve as a rapid screening tool.

    Conclusions:

    • The developed LightGBM model effectively predicts fluorescence lifetimes based on molecular structure and solvent environment.
    • The model learns physically meaningful relationships, identifying key governing factors like quantum yield and molecular geometry.
    • This computational approach offers a valuable tool for accelerating research in fluorescence-based applications.