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A Contrastive-Learning-Based Pre-Training Framework for Optical Property Prediction of Low-Data Rhodamines with

Jiangguo Qiu1, Yanling Wu1, Hong Zhang2

  • 1College of Chemistry, Sichuan University, Chengdu 610064, China.

Molecules (Basel, Switzerland)
|April 14, 2026
PubMed
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This summary is machine-generated.

We developed a new deep learning framework to accurately predict rhodamine probe fluorescence properties. This approach aids in designing advanced fluorescent probes with improved performance.

Area of Science:

  • Computational Chemistry
  • Molecular Design
  • Photophysics

Background:

  • Accurate prediction of rhodamine absorption (λabs) and emission (λemi) wavelengths is critical for designing high-performance fluorescent probes.
  • Limited and heterogeneous optical data for rhodamine derivatives presents challenges for current deep learning models.

Purpose of the Study:

  • To develop a robust deep learning framework for predicting λabs and λemi of rhodamine derivatives.
  • To integrate multi-modal features, including graph representations and solvent descriptors, for enhanced prediction accuracy.
  • To enable mechanism-informed molecular design of novel rhodamine-based probes.

Main Methods:

  • Developed a contrastive-learning-based multitask graph neural network (GNN) framework.
  • Integrated atom-bond level graph representations with solvent descriptors for multi-modal feature input.
Keywords:
contrastive learningfluorescence wavelength predictioninterpretabilitymultitaskrhodamine derivatives

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  • Pre-trained the model on a large dataset of xanthene-derived molecules using self-supervised contrastive learning, followed by fine-tuning on a curated rhodamine dataset.

    • Main Results:

    • Achieved high prediction accuracy with R² values of 0.923 for λabs and 0.913 for λemi.
    • Outperformed existing machine learning, single-task GNN, and non-pre-trained GNN baseline models.
    • Attention-based interpretability identified key chemical regions influencing photophysical properties.
    • Successfully designed three novel rhodamine derivatives with high Stokes shifts, showing minimal deviation between predicted and experimental values.

    Conclusions:

    • The proposed framework enables accurate prediction of rhodamine fluorescence properties.
    • The model provides a promising theoretical guide for designing next-generation fluorescent probes.
    • Demonstrated the utility of deep learning and interpretability for mechanism-informed molecular design.