KPGT-Fluor: A Graph Transformer Framework for Accurate Property Prediction of Fluorescent Dyes under Different...

Data-driven machine learning (ML) technologies have become increasingly prevalent in the prediction of the optical properties of fluorescent dyes, especially across diverse solvent environments─a key requirement for the rational design of small solvatochromic systems. Here, we introduce KPGT-Fluor, a novel adaptation of the Knowledge-guided Pretraining of Graph Transformer (KPGT) framework, designed to model solvent-dependent photophysical behavior. Through the integration of solvent molecular descriptors, KPGT-Fluor effectively captures solvent environmental effects that influence optical properties. KPGT-Fluor exhibits strong predictive performance, achieving mean absolute error (MAE) of 10.55 and 12.09 nm for absorption wavelengths (λ_abs) and emission wavelengths (λ_em), respectively. For the logarithm of the extinction coefficient (ε) and quantum yield (Φ), the MAE values are 0.104 and 0.081, demonstrating a high accuracy. Compared with the existing models, a comprehensive evaluation across the four key property prediction tasks shows that KPGT-Fluor exhibits a more balanced and competitive overall performance. To further demonstrate the effectiveness of the proposed framework, an external test set containing representative main ring structures was selected. Furthermore, two novel D-π-A molecules were synthesized, and their optical properties in different solvents were experimentally compared with KPGT-Fluor predictions. These results highlight KPGT-Fluor as a powerful tool for predicting and discovering solvatochromic materials.