Designing fluorescent small molecules with tailored optical and physicochemical properties requires navigating vast, underexplored chemical space while satisfying multiple objectives and constraints. Conventional generate-score-screen approaches become impractical under such realistic design specifications, owing to their low search efficiency, unreliable generalizability of machine-learning prediction, and the prohibitive cost of quantum chemical calculation. Here we present LUMOS, a data-and-physics driven framework for inverse design of fluorescent molecules. LUMOS couples 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 multi-objective evolutionary algorithms, enabling de novo design and molecular optimization under multiple objectives and constraints. Across comprehensive benchmarks, LUMOS consistently outperforms baseline models in terms of accuracy, generalizability and physical plausibility for fluorescence property prediction, and demonstrates superior performance in multi-objective scaffold- and fragment-level molecular optimization. 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 general fluorophore inverse design.

翻译：设计具有定制光学与理化性质的荧光小分子，需要在广阔且尚未充分探索的化学空间中导航，同时满足多个目标与约束。在如此实际的设计要求下，传统的生成-评分-筛选方法因其搜索效率低下、机器学习预测的泛化能力不可靠以及量子化学计算成本高昂而变得不切实际。本文提出LUMOS，一个用于荧光分子逆向设计的数据与物理双驱动框架。LUMOS在共享的潜在表示中耦合了生成器与预测器，实现了从规格直接到分子的设计以及高效的探索。此外，LUMOS将神经网络与快速的时间相关密度泛函理论（TD-DFT）计算流程相结合，构建了一套在速度、精度和泛化性之间具有不同权衡的互补预测器，从而能够在多样化的场景下实现可靠的性质预测。最后，LUMOS采用了一种与多目标进化算法集成的性质引导扩散模型，实现了在多个目标与约束下的从头设计与分子优化。在全面的基准测试中，LUMOS在荧光性质预测的准确性、泛化性和物理合理性方面持续优于基线模型，并在多目标骨架和片段水平的分子优化中展现出优越性能。使用TD-DFT和分子动力学（MD）模拟进行的进一步验证表明，LUMOS能够生成满足各种目标规格的有效荧光团。总体而言，这些结果确立了LUMOS作为一个数据与物理双驱动的通用荧光团逆向设计框架。