Fluorophores play essential roles in diverse fields such as biochemistry, analytical chemistry, medicine, and spectroscopy. For example, they allow us to observe protein-protein interactions and to screen toxic compounds at the molecular level. However, only a limited number of fluorophores are commonly used. The discovery of novel fluorophores may open new possibilities to observe unseen biological reactions. To find novel and favorable fluorophores, it is essential to identify their photophysical properties accurately, such as absorption, emission wavelength, absorption coefficient, and quantum yield. Conventional approaches to developing fluorophores have limitations. Novel fluorophores have been discovered by modifying existing ones based on human experts' chemical intuition. Despite decades of endeavors by experimental chemists, only a few fluorophores are commonly used, such as fluorescein, BODIPY, cyanine, and Seoul-Fluor. For example, various BODIPY derivatives are discovered by modifying the s-indacene core structure. Seoul-Fluor is developed based on the indolizine core. These molecules are designed to have distinct emission frequencies by attaching diverse functional groups to their core structure. With traditional approaches, finding a novel molecular scaffold takes considerable time and resources to synthesize and verify candidate compounds. Theoretical approaches such as quantum mechanical (QM) calculation or machine learning (ML) may solve this problem more efficiently than conventional approaches. Diverse QM approaches have been developed to predict the photophysical properties of molecules. Most previous studies focused on predicting the photophysical properties of inorganic solids.