Fluor-pred

Fluor-pred is a deep learning-based platform for predicting the photophysical properties of fluorescent molecules. By inputting a SMILES molecular structure and solvent environment, the platform can predict key optical parameters including fluorescence absorption wavelength, emission wavelength, quantum yield, and molar absorption coefficient. It provides data-driven research support for applications in fluorescent probe development, bioimaging studies, and functional materials science.