Designing fluorescent small molecules requires simultaneous control over optical responses, brightness, and physicochemical constraints across vast, underexplored chemical spaces. Conventional generate-score-screen approaches become impractical under such realistic design specifications, owing to their low search efficiency, unreliable generalizability of machine-learning predictions, and the prohibitive cost of quantum chemical calculations. Here, we present LUMOS, a data- and physics-driven framework for inverse design of fluorescent molecules. LUMOS couples the generator and predictor within a shared latent representation, enabling direct specification-to-molecule design and efficient exploration. Moreover, LUMOS combines neural networks with a fast time-dependent density functional theory (TD-DFT) calculation workflow to build a suite of complementary predictors spanning different trade-offs in speed, accuracy, and generalizability, enabling reliable property prediction across diverse scenarios. Finally, LUMOS employs a property-guided diffusion model integrated with multiobjective evolutionary algorithms, enabling de novo design and molecular optimization under multiple objectives and constraints. Benchmarks across random, scaffold, and fluorophore splits show competitive or improved predictive accuracy together with enhanced physical consistency, while multiobjective optimization yields substantially improved Pareto fronts over representative baselines. Further validation using TD-DFT and molecular dynamics (MD) simulations demonstrates that LUMOS can generate valid fluorophores that meet various target specifications. Overall, these results establish LUMOS as a data-physics dual-driven framework for fluorophore inverse design.
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