Quantitative Structure-Fluorescence Property Relationship Analysis of a Large BODIPY Library

Area of Science:

• Computational chemistry
• Photophysics
• Medicinal chemistry

Background:

• BODIPY dyes are versatile fluorophores with excellent photophysical properties.
• Quantitative Structure-Property Relationship (QSPR) analysis can predict molecular properties.
• Developing predictive models for BODIPY fluorescence is crucial for targeted applications.

Purpose of the Study:

• To establish the first quantitative structure-fluorescence property relationship (QSPR) analysis for a large BODIPY library.
• To model and predict absorption (λabs) and fluorescence emission (λem) wavelength maxima of BODIPY derivatives.
• To develop novel descriptors for improved fluorescence property prediction.

Main Methods:

• Utilized stepwise multiple linear regression (MLR) and support vector regression (SVR) for QSPR modeling.
• Performed rigorous model validation using 10-fold cross-validation (CV), y-scrambling CV, and an external validation set.
• Developed and incorporated a novel intramolecular charge transfer descriptor into the QSPR models.

Main Results:

• Non-linear SVR models significantly outperformed linear MLR models, achieving high R² and Q² values (e.g., R²=0.92, Q²=0.71 for λabs).
• Achieved low root mean squared errors (RMSE) of 5.62 nm for λabs and 11.07 nm for λem, confirmed by external validation.
• The novel intramolecular charge transfer descriptor substantially improved the prediction accuracy of λem models.

Conclusions:

• QSPR is an effective tool for modeling and predicting the absorption and emission wavelengths of BODIPY fluorophores.
• SVR provides a robust and accurate method for developing QSPR models for fluorescence properties.
• This QSPR approach facilitates the rational design of BODIPY compounds with tailored fluorescence characteristics within a diversity-oriented fluorescence library approach (DOFLA).