About
This web resource provides everyone with the opportunity to predict the most important spectral properties of BODIPY class compounds using machine learning methods.
Currently, using this resource, you can predict:
- Absorption maximum
- Molar absorption coefficient
- Emission maximum
- Fluorescence lifetime
- Fluorescence quantum yield
- Singlet oxygen generation quantum yield
These properties will be predicted while accounting for the solvent effect.
Method
All models presented here are trained using strict 5-fold cross-validation (5-CV) with CatBoost. RDKit descriptors are selected to describe both the BODIPY structure and the solvent molecule. Additionally, to more accurately account for the solvent's effect on the spectral properties of BODIPY, solvent polarity parameters are also used as descriptors.
Model Performance Metrics
The predictive performance of each model was evaluated using three standard regression metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefficient of determination (R²). The values reported below are averaged across all folds of the 5-fold cross-validation.
| Property | MAE | RMSE | R² |
|---|---|---|---|
| Absorption maximum (nm) | 8.2 | 10.7 | 0.94 |
| Molar absorption coefficient (log ε) | 0.18 | 0.24 | 0.88 |
| Emission maximum (nm) | 9.1 | 11.9 | 0.93 |
| Fluorescence lifetime (ns) | 0.42 | 0.58 | 0.85 |
| Fluorescence quantum yield | 0.07 | 0.10 | 0.82 |
| Singlet oxygen generation quantum yield | 0.09 | 0.12 | 0.79 |
You can learn more about the training protocol in our article.
Dataset
How to use
At the moment, you can use this resource to screen the spectral properties of BODIPYs based on their SMILES representations. To do this, use the "Upload SMILES" block. By uploading an *.xlsx file containing the SMILES of BODIPYs and solvents, you can predict all available spectral properties for a large number of compounds. The prediction results will be automatically downloaded as an *.xlsx file.
Additionally, you can predict all available spectral properties for a single compound by entering its SMILES (or drawing the molecule) and selecting the desired solvent. This feature is available in the "Enter SMILES" block. The prediction results will appear in the "Prediction Results" block below. If needed, you can download the prediction results as an *.xlsx file by clicking Download Results as *.xlsx.
Our Team
Team Leader
Team Member
Team Member
Contacts
The development team consists of researchers from the G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences (Ivanovo, Russia).
If you have any questions, comments, or suggestions about SpecML, feel free to contact us via
Our projects
This research was funded by the Russian Science Foundation (grant number 24-73-00006).
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