Fluorophore Design via Generative Modeling in a Curated Subspace.

We present a fluorescence-oriented molecular design strategy that integrates multiple generative models within a chemically constrained and photophysically relevant subspace. Rather than relying on large-scale data sets or model-specific tuning, our approach builds on a curated training set constructed exclusively from atom-in-SMILES (AIS) fragments spanning chromophore-like molecules. The pipeline accommodates reinforcement learning-based ReLeaSE, which employs AIS-based tokenization, MolDQN, which utilizes a graph-based molecular representation, and the combinatorial optimization-based MolFinder, which operates on SMILES strings, each guided by a semiheuristic objective function targeting excitation energy, oscillator strength, and fluorophore similarity. Despite the compact training corpus, the pipeline yields structurally novel and optically active molecules, validated via QM calculations at the sTDA/CAM-B3LYP level. Our findings demonstrate that strategic data curation and property-driven integration can enable general-purpose models to succeed in specialized discovery tasks such as de novo fluorophore design.